EP2673777B1 - Suppression de bruit combinée et signaux hors emplacement - Google Patents

Suppression de bruit combinée et signaux hors emplacement Download PDF

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Publication number
EP2673777B1
EP2673777B1 EP12707412.8A EP12707412A EP2673777B1 EP 2673777 B1 EP2673777 B1 EP 2673777B1 EP 12707412 A EP12707412 A EP 12707412A EP 2673777 B1 EP2673777 B1 EP 2673777B1
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Prior art keywords
gain
noise
banded
suppression
signal
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German (de)
English (en)
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EP2673777A1 (fr
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Glenn N. Dickins
Timothy J. NEAL
Mark S. Vinton
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Dolby Laboratories Licensing Corp
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Dolby Laboratories Licensing Corp
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L2021/02161—Number of inputs available containing the signal or the noise to be suppressed
    • G10L2021/02166—Microphone arrays; Beamforming

Definitions

  • the present disclosure relates generally to acoustic signal processing, and in particular, to processing of sound signals to suppress undesired signals such as noise, echoes, and out-of-location signals.
  • Acoustic signal processing is applicable today to improve the quality of sound signals such as from microphones.
  • many devices such as handsets operate in the presence of sources of echoes, e.g., loudspeakers.
  • signals from microphones may occur in a noisy environment, e.g., in a car or in the presence of other noise.
  • there may be sounds from interfering locations e.g., out-of-location conversation by others, or out-of-location interference, wind, etc. Acoustic signal processing is therefore an important area for invention.
  • a dynamic suppression element prior to echo control can destabilize echo estimation.
  • the alternative of having echo control first adds computational complexity. It is desirable to create a system that can retain a stable operation and avoid unnatural sounding output in the presence of voice, noise and echo, especially when the power in the desired signal is becomes low or comparable to the undesired signals.
  • Embodiments of the present invention include a system according to claim 1, a method according to claim 13, and a non-transitory computer-readable medium according to claim 15.
  • the method is to process a plurality of input signals, e.g., microphone signals to simultaneously suppress noise, out-of-location signals, and in some embodiments, echoes.
  • Embodiments of the invention process sampled data in frames of samples, frame-by-frame.
  • the term "instantaneous" in the context of such processing frame-by-frame means for the current frame.
  • Particular embodiments include a system comprising an input processor to accept a plurality of sampled input signals and form a mixed-down banded instantaneous frequency domain amplitude metric of the input signals for a plurality of frequency bands.
  • the input processor includes input transformers to transform to frequency bins, a downmixer, e.g., beamformer to form a mixed-down, e.g., beamformed signal, and a spectral banding element to form frequency bands.
  • the downmixing e.g., beamforming is carried out prior to transforming, and in others, the transforming is prior to downmixing, e.g., beamforming.
  • One system embodiment includes a banded spatial feature estimator to estimate banded spatial features from the plurality of sampled input signals, e.g., after transforming, and in other embodiments, before transforming.
  • Versions of the system that include echo suppression include a reference signal input processor to accept one or more reference signals, a transformer and a spectral banding element to form a banded frequency domain amplitude metric representation of the one or more reference signals.
  • Such versions of the system include a predictor of a banded frequency domain amplitude metric representation of the echo based on adaptively determined filter coefficients.
  • a noise estimator determines an estimate of the banded spectral amplitude metric of the noise.
  • a voice-activity detector uses the banded spectral amplitude metric of the noise, an estimate of the banded spectral amplitude metric of the mixed-down signal determined by a signal spectral estimator, and previously predicted echo spectral content to ascertain whether there is voice or not.
  • the banded signal is a sufficiently accurate estimate of the banded spectral amplitude metric of the mixed-down signal, so that signal spectral estimator is not used.
  • the output of the VAD is used by an adaptive filter updater to determine whether or not to update the filter coefficients, the updating based on the estimates of the banded spectral amplitude metric of the mixed-down signal and of the noise, and the previously predicted echo spectral content.
  • the system further includes a gain calculator to calculate suppression probability indicators, e.g., as gains including, in one embodiment, an out-of-location signal probability indicator, e.g., out-of-location gain determined using two or more of the spatial features, and a noise suppression probability indicator, e.g., noise suppression gain determined using an estimate of noise spectral content.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content.
  • the noise suppression probability indicator e.g., suppression gain includes echo suppression.
  • the gain calculator further is to combine the raw suppression probability indicators, e.g., suppression gains to a first combined gain for each band.
  • the gain calculator further calculates an additional echo suppression probability indicator, e.g., an echo suppression gain. In one embodiment this is combined with the other gains (prior to post-processing in embodiments that include post-processing) to form the first combined gain, which is a final gain. In another embodiment, the additional echo suppression probability indicator, e.g., suppression gain is combined, with the results of post-processing in embodiments that include post-processing, otherwise with the first combined gain to generate the final gain.
  • an additional echo suppression probability indicator e.g., suppression gain
  • the system further includes a noise suppressor that interpolates the final gain to produce final bin gains and to apply the final bin gains to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data.
  • the system further includes one or both of: a) an output synthesizer and transformer to generate output samples in the time domain, and b) output remapping to generate output frequency bins suitable for use by a subsequent codec or processing stage.
  • One system embodiment includes means for determining banded spatial features from the plurality of sampled input signals.
  • Some system embodiments that include echo suppression include means for accepting one or more reference signals and for forming a banded frequency domain amplitude metric representation of the one or more reference signals, and means for predicting a banded frequency domain amplitude metric representation of the echo.
  • the means for predicting includes means for adaptively determining echo filter coefficients coupled to means for determining an estimate of the banded spectral amplitude metric of the noise, means for voice-activity detecting (VAD) using the estimate of the banded spectral amplitude metric of the mixed-down signal, and means for updating the filter coefficients based on the estimates of the banded spectral amplitude metric of the mixed-down signal and of the noise, and the previously predicted echo spectral content.
  • the means for updating updates according to the output of the means for voice activity detecting.
  • One system embodiment further includes means for calculating suppression probability indicators, e.g., suppression gains including an out-of-location signal gain determined using two or more of the spatial features, and a noise suppression probability indicator, e.g., noise suppression gain determined using an estimate noise spectral content.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content.
  • the noise suppression probability indicator e.g., suppression gain includes echo suppression.
  • the calculating by the means for calculating includes combining the raw suppression probability indicators, e.g., suppression gains to form a first combined gain for each band.
  • the means for calculating further includes means for carrying out post-processing on the first combined gains of the bands to generate a post-processed gain for each band.
  • the post-processing includes depending on the embodiment, one or more of: ensuring minimum gain, in some embodiments in a band dependent manner; in some embodiments ensuring there are no outlier or isolated gains by carrying out median filtering of the combined gain; and in some embodiments ensuring smoothness by carrying out time smoothing and, in some embodiments, band-to-band smoothing.
  • the means for post-processing includes means for spatially-selective voice activity detecting using two or more of the spatial features to generate a signal classification, such that the post-processing is according to the signal classification.
  • the means for calculating includes means for calculating an additional echo suppression probability indicator, e.g., suppression gain. This is combined in some embodiments with gain(s) (prior to post-processing in embodiments that include post-processing) to form the first combined gain, with the post-processing first combined gain forming a final gain, and in other embodiments, the additional echo suppression probability indicator, e.g., suppression gain is combined with the results of post-processing in embodiments that include post-processing, otherwise with the first combined gain to generate a final gain.
  • an additional echo suppression probability indicator e.g., suppression gain.
  • One system embodiment further includes means for interpolating the final gain to bin gains and for applying the final bin gains to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data.
  • One system embodiment further includes means for applying one or both of: a) output synthesis and transforming to generate output samples, and b) output remapping to generate output frequency bins.
  • Particular embodiments include a processing apparatus comprising a processing system and configured to suppress undesired signals including noise and out-of-location signals, the processing apparatus configured to: accept a plurality of sampled input signals and form a mixed-down banded instantaneous frequency domain amplitude metric of the input signals for a plurality of frequency bands, the forming including transforming into complex-valued frequency domain values for a set of frequency bins.
  • the processing apparatus is further configured to determine banded spatial features from the plurality of sampled input signals; to calculate a first set of suppression probability indicators, including an out-of-location suppression probability indicator determined using two or more of the spatial features, and a noise suppression probability indicator for each band determined using an estimate of noise spectral content; to combine the first set of probability indicators to determine a first combined gain for each band; and to apply an interpolated final gain determined from the first combined gain to carry out suppression on bin data of the mixed-down signal to form suppressed signal data.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content determined using two or more of the spatial features.
  • Particular embodiments include a method of operating a processing apparatus to suppress noise and out-of-location signals and in some embodiments echo.
  • the method comprises: accepting in the processing apparatus a plurality of sampled input signals, and forming a mixed-down banded instantaneous frequency domain amplitude metric of the input signals for a plurality of frequency bands, the forming including downmixing, e.g., transforming into complex-valued frequency domain values for a set of frequency bins.
  • the forming includes transforming the input signals to frequency bins, downmixing, e.g., beamforming the frequency data, and banding.
  • the downmixing can be before transforming, so that a single mixed-down signal is transformed.
  • the method includes determining banded spatial features from the plurality of sampled input signals.
  • the method includes accepting one or more reference signals and forming a banded frequency domain amplitude metric representation of the one or more reference signals.
  • the representation in one embodiment is the sum.
  • the method includes predicting a banded frequency domain amplitude metric representation of the echo using adaptively updated echo filter coefficients, the coefficients updated using an estimate of the banded spectral amplitude metric of the noise, previously predicted echo spectral content, and an estimate of the banded spectral amplitude metric of the mixed-down signal.
  • the estimate of the banded spectral amplitude metric of the mixed-down signal is in one embodiment the mixed-down banded instantaneous frequency domain amplitude metric of the input signals, while in other embodiments, signal spectral estimation is used.
  • the control of the update of the prediction filter in one embodiment further includes voice-activity detecting-VAD-using the estimate of the banded spectral amplitude metric of the mixed-down signal, the estimate of banded spectral amplitude metric of noise, and the previously predicted echo spectral content.
  • the results of voice-activity detecting determine whether there is updating of the filter coefficients.
  • the updating of the filter coefficients is based on the estimates of the banded spectral amplitude metric of the mixed-down signal and of the noise, and the previously predicted echo spectral content.
  • the method includes calculating raw suppression probability indicators, e.g., suppression gains including an out-of-location signal gain determined using two or more of the spatial features and a noise suppression probability indicator, e.g., as a noise suppression gain determined using an estimate of noise spectral content, and combining the raw suppression probability indicators, e.g., suppression gains to determine a first combined gain for each band.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content.
  • the noise suppression probability indicator, e.g., suppression gain in some embodiments includes suppression of echoes, and its calculating also uses the predicted echo spectral content.
  • the method further includes carrying out spatially-selective voice activity detection determined using two or more of the spatial features to generate a signal classification, e.g., whether the input audio signal is voice or not.
  • a signal classification e.g., whether the input audio signal is voice or not.
  • wind detection is used, such that the signal classification further includes whether the input audio signal is wind or not.
  • Some embodiments of the method further include carrying out post-processing on the first combined gains of the bands to generate a post-processed gain for each band.
  • the post-processing includes in some embodiments one or more of: ensuring minimum gain, e.g., in a band dependent manner, ensuring there are no isolated or outlier gains by carrying out median filtering of the combined gain, and ensuring smoothness by carrying out time and/or band-to-band smoothing.
  • the post-processing is according to the signal classification.
  • the method includes calculating an additional echo suppression probability indicator, e.g., suppression gain.
  • the additional echo suppression gain is combined with the other raw suppression gains to form the first combined gain, and (post-processed if post-processing is included) first combined gain forms a final gain for each band.
  • the additional echo suppression gain is combined with the (post-processed if post-processing is included) first combined gain to generate a final gain for each band.
  • the method includes interpolating the final gain to produce final bin gains, and applying the final bin gains to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data, and applying one or both of a) output synthesis and transforming to generate output samples, and b) output remapping to generate output frequency bins.
  • Particular embodiments include a method of operating a processing apparatus to suppress undesired signals, the undesired signals including noise.
  • Particular embodiments also include a processing apparatus including a processing system, with the processing apparatus configured to carry out the method. The method comprises: accepting in the processing apparatus at least one sampled input signal; and forming a banded instantaneous frequency domain amplitude metric of the at least one input signal for a plurality of frequency bands, the forming including transforming into complex-valued frequency domain values for a set of frequency bins.
  • the method further comprises calculating a first set of one or more suppression probability indicators, including a noise suppression probability indicator determined using an estimate of noise spectral content; combining the first set of probability indicators to determine a first combined gain for each band; and applying an interpolated final gain determined from the first combined gain to carry out suppression on bin data of the at least one input signal to form suppressed signal data.
  • the noise suppression probability indicator for each frequency band is expressible as noise suppression gain function of the banded instantaneous amplitude metric for the band. For each frequency band, a first range of values of banded instantaneous amplitude metric values is expected for noise, and a second range of values of banded instantaneous amplitude metric values is expected for a desired input.
  • the noise suppression gain functions for the frequency bands are configured to: have a respective minimum value; have a relatively constant value or a relatively small negative gradient in the first range; have a relatively constant gain in the second range; and have a smooth transition from the first range to the second range.
  • Particular embodiments include a method of operating a processing apparatus to suppress undesired signals.
  • the method comprises: accepting in the processing apparatus at least one sampled input signal; forming a banded instantaneous frequency domain amplitude metric of the at least one input signal for a plurality of frequency bands, the forming including transforming into complex-valued frequency domain values for a set of frequency bins; calculating a first set of one or more suppression probability indicators, including a noise suppression probability indicator determined using an estimate of noise spectral content; and combining the first set of probability indicators to determine a first combined gain for each band.
  • Some embodiments of the method further comprise carrying out post-processing on the first combined gains of the bands to generate a post-processed gain for each band, the post-processing including ensuring minimum gains for each band; and applying an interpolated final gain determined from the post-processed gain to carry out suppression on bin data of the at least one input signal to form suppressed signal data.
  • the post-processing includes one or more of: carrying out median filtering of gains; carrying out band-to-band smoothing of gains, and carrying out time smoothing of gains.
  • Particular embodiments include a method of operating a processing apparatus to process at least one sampled input signal, the method comprising: accepting in the processing apparatus at least one sampled input signal and forming a banded instantaneous frequency domain amplitude metric of the at least one input signal for a plurality of frequency bands, the forming including transforming into complex-valued frequency domain values for a set of frequency bins and banding to a plurality of frequency bands.
  • the method further includes calculating a gain for each band in order to achieve noise reduction and/or, in the case that the banding is perceptual banding, one or more of perceptual domain-based leveling, perceptual domain-based dynamic range control, and perceptual domain-based dynamic equalization.
  • the method further comprises carrying out post-processing on the gains of the bands to generate a post-processed gain for each band; the post-processing including median filtering of the gains of the bands, and applying an interpolated final gain determined from the (post-processed if post-processing is included) gain to carry out noise reduction and/or, in the case that the banding is perceptual banding, one or more of perceptual domain-based leveling, perceptual domain-based dynamic range control, and perceptual domain-based dynamic equalization on bin data to form processed signal data.
  • Some versions of the method further comprise carrying out at least one of voice activity detecting and wind activity detecting to a signal classification, wherein the median filtering depends on the signal classification.
  • Particular embodiments include a method of operating a processing apparatus to suppress undesired signals, the method comprising: accepting in the processing apparatus a plurality of sampled input signals; and forming a mixed-down banded instantaneous frequency domain amplitude metric of the input signals for a plurality of frequency bands, the forming including transforming into complex-valued frequency domain values for a set of frequency bins.
  • the method further comprises determining banded spatial features from the plurality of sampled input signals; calculating a first set of suppression probability indicators, including an out-of-location suppression probability indicator determined using two or more of the spatial features, and a noise suppression probability indicator determined using an estimate of noise spectral content; combining the first set of probability indicators to determine a first combined gain for each band.
  • the first combined gain after post-processing if post-processing is included, forms a final gain for each band. ; and applying an interpolated final gain determined from the first combined gain. Interpolating the final gain produces final bin gains to apply to bin data of the mixed-down signal to form suppressed signal data.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content determined using two or more of the spatial features.
  • the estimate noise spectral content is determined by a leaky minimum follower with a tracking rate defined by at least one minimum follower leak rate parameter.
  • the at least one leak rate parameter of the leaky minimum follower are controlled by the probability of voice being present as determined by voice activity detecting.
  • the method further comprises calculating a first set of suppression probability indicators, including an out-of-location suppression probability indicator determined using two or more of the spatial features, and a noise suppression probability indicator determined using an estimate of noise spectral content; accepting in the processing apparatus one or more reference signals; forming a banded frequency domain amplitude metric representation of the one or more reference signals; and predicting a banded frequency domain amplitude metric representation of an echo using adaptively determined echo filter coefficients.
  • the method further includes determining a plurality of indications of voice activity from the mixed-down banded instantaneous frequency domain amplitude metric using respective instantiations of a universal voice activity detection method, the universal voice activity detection method controlled by a set of parameters and using: an estimate of noise spectral content, the banded frequency domain amplitude metric representation of the echo, and the banded spatial features, the set of parameters including whether the estimate of noise spectral content is spatially selective or not, which indication of voice activity an instantiation determines being controlled by a selection of the parameters, voice activity.
  • the method further comprises combining the first set of probability indicators to determine a first combined gain for each band; and applying an interpolated final gain determined from the gain (post-processed, if post-processing is included) to carry out suppression on bin data of the mixed-down signal to form suppressed signal data.
  • Different instantiations of the universal voice activity detection method are applied in different steps of the method.
  • the estimate of noise spectral content is a spatially-selective estimate of noise spectral content determined using two or more of the spatial features.
  • Particular embodiments include a tangible computer-readable storage medium configured with instructions that when executed by at least one processor of a processing system, cause processing hardware to carry out a method as described herein.
  • Particular embodiments include logic that can be encoded in one or more computer-readable tangible media to carry out a method as described herein.
  • Particular embodiments may provide all, some, or none of these aspects, features, or advantages. Particular embodiments may provide one or more other aspects, features, or advantages, one or more of which may be readily apparent to a person skilled in the art from the figures, descriptions, and claims herein.
  • Described herein is a method of processing: (a) a plurality of input signals, e.g., signals from a plurality of spatially separated microphones; and, for echo suppression, (b) one or more reference signals, e.g., signals from or to be rendered by one or more loudspeakers and that can cause echoes.
  • a source of sound e.g., a human who is a source of human voice for the array of microphones.
  • the method processes the input signals and one or more reference signals to carry out in an integrated manner simultaneous noise suppression, echo suppression, and out-of-location signal suppression.
  • Also described herein is a system accepting the plurality of input signals and the one or more reference signals to process the input signals and one or more reference signals to carry out in an integrated manner simultaneous noise suppression, echo suppression, and out-of-location signal suppression.
  • at least one storage medium on which are coded instructions that when executed by one or more processors of a processing system, cause processing a plurality of input signals, e.g., microphone signals and one or more reference signals, e.g., for or from one or more loudspeakers to carry out in an integrated manner simultaneous noise suppression, echo suppression, and out-of-location signal suppression.
  • Embodiments of the invention are described in terms of determining and applying a set of suppression probability indicators, expressed, e.g., as suppression gains for each of a plurality of spectral bands, applied to spectral values of signals at a number of frequency bands.
  • the spectral values represent spectral content.
  • the spectral content is in terms of the power spectrum.
  • the invention is not limited to processing power spectral values. Rather, any spectral amplitude dependent metric can be used. For example, if the amplitude spectrum is used directly, such spectral content is sometimes referred to as spectral envelope.
  • the phrase “power spectrum (or other amplitude metric spectrum)" is used in the description.
  • FIG. 1 shows a block diagram of an embodiment of a system 100 that accepts a number of one or more denoted P of signal inputs 101, e.g., microphone inputs from microphones (not shown) at different respective spatial locations, the input signals denoted MIC 1, ..., MIC P, and a number, denoted Q of reference inputs 102, denoted REF 1, ..., REF Q, e.g., Q inputs 102 to be rendered on Q loudspeakers, or signals obtained from Q loudspeakers.
  • the signals 101 and 102 are in the form of sample values.
  • P 1, i.e., there is only a single microphone inputs.
  • the system 100 shown in FIG. 1 carries out in an integrated manner simultaneous noise suppression and out-of-location signal suppression, and in some embodiments also simultaneous echo suppression.
  • One such embodiment includes a system 100 comprising an input processor 103, 107, 109 to accept a plurality of sampled input signals and form a mixed-down banded instantaneous frequency domain amplitude metric 110 of the input signals 101 for a plurality B of frequency bands.
  • the beamforming is carried out prior to transforming, and in others, as shown in FIG. 1 , the transforming is prior to downmixing, e.g., beamforming.
  • One system embodiment includes a banded spatial feature estimator 105 to estimate banded spatial features 106 from the plurality of sampled input signals, e.g., after transforming, and in other embodiments, before transforming.
  • Versions of system 100 that include echo suppression include a reference signal input processor 111 to accept one or more reference signals, a transformer 113 and a spectral banding element 115 to form a banded frequency domain amplitude metric representation 116 of the one or more reference signals.
  • Such versions of system 100 include a predictor 117 of a banded frequency domain amplitude metric representation of the echo 118 based on adaptively determined filter coefficients.
  • a noise estimator 123 determines an estimate of the banded spectral amplitude metric of the noise 124.
  • a voice-activity detector (VAD) 124 uses the banded spectral amplitude metric of the noise 124, an estimate of the banded spectral amplitude metric of the mixed-down signal 122 determined by a signal spectral estimator 121, and previously predicted echo spectral content 118 to produce a voice detection output.
  • the banded signal 110 is a sufficiently accurate estimate of the banded spectral amplitude metric of the mixed-down signal 122, so that signal spectral estimator 121 is not used.
  • the results of the VAD 125 are used by an adaptive filter updater 127 to determine whether to update the filter coefficients 128 based on the estimates of the banded spectral amplitude metric of the mixed-down signal 122 (or 110) and of the noise 124, and the previously predicted echo spectral content 118.
  • System 100 further includes a gain calculator 129 to calculate suppression probability indicators, e.g., as gains including, in one embodiment, an out-of-location signal probability indicator, e.g., gain determined using two or more of the spatial features 106, and a noise suppression probability indicator, e.g., gain determined using spatially-selective noise spectral content.
  • the noise suppression gain includes echo suppression.
  • the gain calculator 129 further is to combine the raw suppression gains to a first combined gain for each band.
  • gain calculator 129 further is to carry out post-processing on the first combined gains of the bands to generate a post-processed gain 130 for each band.
  • the post-processing includes depending on the embodiment, one or more of: ensuring minimum gain, in some embodiments in a band dependent manner; in some embodiments ensuring there are no outlier or isolated gains by carrying out median filtering of the combined gain; and in some embodiments ensuring smoothness by carrying out time smoothing and, in some embodiments, band-to-band smoothing.
  • the post-processing includes spatially-selective voice activity detecting using two or more of the spatial features 106 to generate a signal classification, such that the post-processing is according to the signal classification.
  • the gain calculator 129 further calculates an additional echo suppression gain. In one embodiment this is combined with the other gains (prior to post-processing, if post-processing is included) to form the first combined gain. In another embodiment, the additional echo suppression gain is combined with the first combined gain (after post-processing, if post-processing is included) to generate a final gain for each band.
  • System 100 further includes a noise suppressor 131 to apply the gain 130 (after post-processing, if post-processing is included) to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data 132.
  • System 100 further includes in 133 one or both of: a) an output synthesizer and transformer to generate output samples, and b) output remapping to generate output frequency bins.
  • the system embodiments that include echo suppression include means for accepting 213 one or more reference signals and for forming 215, 217 a banded frequency domain amplitude metric representation 116 of the one or more reference signals, and means for predicting 117, 123, 125, 127 a banded frequency domain amplitude metric representation of the echo 118.
  • the means for predicting 117, 123, 125, 127 includes means for adaptively determining 125, 127 echo filter coefficients 128 coupled to means for determining 123 an estimate of the banded spectral amplitude metric of the noise 124, means for voice-activity detecting (VAD) using the estimate of the banded spectral amplitude metric of the mixed-down signal 122, and means for updating 127 the filter coefficients 128.
  • VAD voice-activity detecting
  • the output of the VAD is coupled to means for updating and determined if the means for updating updates the filter coefficients.
  • the filter coefficients are updated based on the estimates of the banded spectral amplitude metric of the mixed-down signal 122 and of the noise 124, and the previously predicted echo spectral content 118;
  • the means for calculating 129 further includes means for carrying out post-processing on the first combined gains of the bands to generate a post-processed gain 130 for each band.
  • the post-processing includes in some embodiments one or more of ensuring minimum gain, e.g., in a band dependent manner, ensuring there are no isolated gains by carrying out median filtering of the combined gain, and ensuring smoothness by carrying out time and/or band-to-band smoothing.
  • the means for post-processing includes means for spatially-selective voice activity detecting using two or more of the spatial features 106 to generate a signal classification, such that the post-processing is according to the signal classification.
  • the means for calculating 129 includes means for calculating an additional echo suppression gain. This is combined in some embodiments with gain(s) (prior to post-processing, if post-processing is included) to form the first combined gains of the bands to be used as a final gain for each band, and in other embodiments the additional echo suppression gain in each band is combined with the first combined gains (post-processed, if post-processing is included) to generate a final gain for each band.
  • One system embodiment further includes means 131 for interpolating the final gains to final bin gains and applying the final bin gains to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data 132.
  • One system embodiment further includes means 133 for applying one or both of: a) output synthesis and transforming to generate output samples 135, and b) output remapping to generate output frequency bins 135 (note the same reference numeral is used for both an output sample generator, and an output frequency bin generator).
  • FIG. 2 shows a flowchart of a method 200 of operating a processing apparatus 100 to suppress noise and out-of-location signals and in some embodiments echo in a number denoted P of signal inputs 101, e.g., microphone inputs from microphones at different respective spatial locations, the input signals denoted MIC 1, ..., MIC P.
  • method 200 includes processing a number, denoted Q of reference inputs 102, denoted REF 1, ..., REF Q, e.g., Q inputs to be rendered on Q loudspeakers, or signals obtained from Q loudspeakers.
  • the signals are in the form of sample values.
  • the system carries out, in an integrated manner, simultaneous noise suppression, out-of-location signal suppression, and, in some embodiments, echo suppression.
  • method 200 comprises: accepting 201 in the processing apparatus a plurality of sampled input signals 101, and forming 203, 207, 209 a mixed-down banded instantaneous frequency domain amplitude metric 110 of the input signals 101 for a plurality of frequency bands, the forming including transforming 203 into complex-valued frequency domain values for a set of frequency bins.
  • the forming includes in 203 transforming the input signals to frequency bins, downmixing, e.g., beamforming the frequency data, and in 207 banding.
  • the downmixing can be before transforming, so that a single mixed-down signal is transformed.
  • the system may make use of an estimate of the banded echo reference, or a similar representation of the frequency domain spectrum of the echo reference provided by another processing component or source within the realized system.
  • the method includes determining in 205 banded spatial features 106 from the plurality of sampled input signals.
  • the method includes accepting 213 one or more reference signals and forming in 215 and 217 a banded frequency domain amplitude metric representation 116 of the one or more reference signals.
  • the representation in one embodiment is the sum.
  • the method includes predicting in 221 a banded frequency domain amplitude metric representation of the echo 118 using adaptively determined echo filter coefficients 128.
  • the predicting in one embodiment further includes voice-activity detecting-VAD-using the estimate of the banded spectral amplitude metric of the mixed-down signal 122, the estimate of banded spectral amplitude metric of noise 124, and the previously predicted echo spectral content 118.
  • the method 200 includes: a) calculating in 223 raw suppression gains including an out-of-location signal gain determined using two or more of the spatial features 106, and a noise suppression gain determined using spatially-selective noise spectral content; and b) combining the raw suppression gains to a first combined gain for each band.
  • the noise suppression gain in some embodiments includes suppression of echoes, and its calculating 223 also uses the predicted echo spectral content 118.
  • the method 200 further includes carrying out in spatially-selective voice activity detection determined using two or more of the spatial features 106 to generate a signal classification, e.g., whether voice or not.
  • a signal classification e.g., whether voice or not.
  • wind detection is used such that the signal classification further includes whether the signal is wind or not.
  • the method includes calculating in 226 an additional echo suppression gain.
  • the additional echo suppression gain is included in the first combined gain which is used as a final gain for each band, and in other embodiment, the additional echo suppression gain is combined with the first combined gain (post-processed, if post-processing is included) to generate a final gain for each band.
  • the method includes applying in 227 the final gain, including interpolating the gain for bin data to carry out suppression on the bin data of the mixed-down signal to form suppressed signal data 132. And apply in 229 one or both of a) output synthesis and transforming to generate output samples, and b) output remapping to generate output frequency bins.
  • FIG. 2 Whilst the disclosure is presented for a complete method ( FIG. 2 ), system or apparatus ( FIG. 1 ) that includes all aspects of suppression, including simultaneous echo, noise, and out-of-spatial location suppression, or presented as a computer-readable storage medium that includes instructions that when executed by one or more processors of a processing system (see FIG. 16 and description thereof), cause a processing apparatus that includes the processing system to carry out the method such as that of FIG. 2 , note that the example embodiments also provide a scalable solution for simpler applications and situations.
  • One embodiment includes simultaneous noise suppression, echo suppression and out-of-spatial location suppression, while another embodiment includes simultaneous noise suppression and out-of-spatial location suppression.
  • One embodiment includes simultaneous noise suppression, echo suppression and out-of-spatial location suppression, while another embodiment includes simultaneous noise suppression and out-of-spatial location suppression.
  • the Q reference signals represent a set of audio signals that relate to the potential echo at the microphone array.
  • the microphone array may be that of a headset, personal mobile device or fixed microphone array.
  • the references may correspond to signals being used to drive one or several speakers on the headset or personal mobile device, or one or more speakers used in a speaker array or surround sound configuration, or the loudspeakers on a portable device such as a laptop computer or tablet. It is noted that the application is not limited to these scenarios, however the nature of the approach is best suited to an environment where the response from each reference to the microphone array center is similar in gain and delay.
  • the reference signals may also represent a signal representation prior to the actual speaker feeds, for example a raw audio stream prior to it being rendered and sent to a multichannel speaker output.
  • the proposed approach offers a solution for robust echo control which also allows for moderate spatial and temporal variation in the echo path, including being robust to sampling offsets, discontinuities and timing drift.
  • the output of the system is a single signal representing the separated voice or signal of interest after the removal of noise, echo and sound components not originating from the desired position.
  • the output of the system is a set of remapped frequency components representing the separated voice or signal of interest after the removal of noise, echo and sound components not originating from the desired position. These frequency components are, e.g., in a form usable by a subsequent compression (coding) method or additional processing component.
  • Each of the processing of system 100 and the method 200 is carried out in a frame-based manner (also called block-based manner) on a frame of M input samples (also called a block of M input samples) at each processing time instant.
  • the P inputs e.g., microphone inputs are transformed by one or more time-to-frequency transformers 103 independently to produce a set of P frequency domain representations.
  • the transform to the frequency domain representation will typically have a set of N linearly spaced frequency bins each having a single complex value at each processing time instant. It is noted that generally N ⁇ M such that at each time instant, M new audio data samples are processed to create N complex-valued frequency domain representation data points.
  • the increased data in the complex-valued frequency domain representation allows for a degree of analysis and processing of the audio signal suited to the noise, echo and spatial selectivity algorithm to achieve reasonable phase estimation.
  • the Q reference inputs are combined using a simple time domain sum. This creates a single reference signal of M real-valued samples at each processing instant. It has been found by the inventor(s) that the system is able to achieve suppression for a multi-channel echo by using only a single combined reference. While the invention does not depend on any reasoning of why the results are achieved, it is believed that using only a single combined reference works, we believe, as a result of the inherent robustness of using the banded amplitude metric representation of the echo, noise and signal within the suppression framework, and the broader time resolution offered from the time-frame-based processing. This approach allows a certain timing and gain uncertainty or margin of error.
  • the Q reference inputs are combined, e.g., using summation in the time domain to create a single reference signal to be used for the echo control. In some embodiments, this summation may occur after the transform or at the banding stage where the power spectra (or other amplitude metric spectra) of the Q reference signals may be combined. Combining the signals in the power domain has the advantage of avoiding the effects of destructive (cancellation) or constructive combination of correlated content across the Q signals. Such 'in phase' or exact phase aligned combination of the reference signals is unlikely to occur extensively and consistently across time and/or frequency at the microphones due to the inherent complexities of the expected acoustic echo paths.
  • the direct combination approach can create deviations in the single channel reference power estimate and its ability to be used as an echo predictor. In practice, this is not found to be a significant problem for typical multi channel content.
  • the single channel time domain summation offers effective performance at very low complexity. Where a large amount of correlated content is expected between the channels, and the probability is reasonable that there may be opposing phase and time aligned content, the potential for loss of echo control performance can be reduced by using a de-correlating filter on one or more of the reference channels.
  • a de-correlating filter on one or more of the reference channels.
  • a de-correlating filter on one or more of the reference channels.
  • a time delay A 2-5ms time delay is suggested for such embodiments of the invention.
  • Another example is a bulk phase shift such as a Hilbert transform or 90-degree phase shift.
  • Embodiments of the invention process the data frame-by-frame, with each consecutive frame of samples used in the transform overlapping with the previous frame of samples used in some way. Such overlapped frame processing is common in audio signal processing.
  • the term "instantaneous" as used herein in the context of such frame-by-frame processing means for the current frame.
  • FIGS. 3A-3E show some details of some of the elements of embodiments of the invention.
  • FIG. 3A shows a frame (a block) of M input samples being placed in a buffer of length 2 N with a set of 2N - M previous samples and being windowed according to a window function to generate 2 N values which are transformed according to a transform, with an additional twist function as described below. This results in N complex-valued bins.
  • FIG. 3B shows the conversion of the N bins to a number B of frequency bands. The banding to B bands is described in more detail below.
  • One aspect of the invention is the determination of a set of B suppression gains for the B bands. The determination of the gains incorporates statistical spatial information, e.g., indicative of out-of-location signals.
  • FIG. 3C shows the interpolation of B gains to create a set of N gains which are then applied to N bins of input data.
  • Some embodiments of the invention include post-processing of raw-gains to ensure stability.
  • the post-processing is controlled based on signal classification, e.g., a classification of the signal to according to one or more of (spatially selective) voice activity and wind activity.
  • the post-processing applied is selected according to signal activity classification.
  • the post-processing includes preventing the gains from falling below some pre-specified (frequency-band-dependent) minimum point, the manner of prevention dependent on the activity classification, how musical noise due to one or more isolated gain values can be effectively eliminated in a manner dependent on the activity classification, and how the gains may be smoothed, with the type and amount of smoothing dependent on the activity classification.
  • FIG. 3D describes the synthesis process of converting the N output bins to a frame of M output samples, and typically involves inverse transforming and windowed overlap-add operations.
  • FIG. 3E is an optional output stage which can reformat the N complex-valued bins from FIG. 3C to suit the transform needs of subsequent processing (such as an audio codec) thus saving processing time and reducing signal latency.
  • the processing of FIG. 3D is not used, as the output is to be encoded in some manner. In such cases, a remap operation as shown in FIG. 3E is applied.
  • DFT discrete finite length Fourier transform
  • FFT fast Fourier transform
  • a discrete finite length Fourier transform, such as implemented by the FFT is often referred to as a circulant transform due to the implicit assumption that the signal in the transform window is in some way periodic or repetitive.
  • Most general forms of circulant transforms can be represented by buffering, a window, a twist (real value to complex value transformation) and a DFT, e.g., FFT.
  • An optional complex twist after the DFT can be used to adjust the frequency domain representation to match specific transform definitions.
  • This class of transforms includes the modified DFT (MDFT), the short time Fourier transform (STFT) and with a longer window and wrapping, a conjugate quadrature mirror filter (CQMF).
  • MDFT modified DFT
  • STFT short time Fourier transform
  • CQMF conjugate quadrature mirror filter
  • MDCT Modified discrete cosine transform
  • MDST modified discrete sine transform
  • the additional complex twist of the frequency domain bins is used, however this does not change the underlying frequency resolution or processing ability of the transform and thus can be left until the end of the processing chain, and applied in the remapping if required.
  • the following transform and inverse pair is used for the forward transform of FIG. 3A and inverse transform of FIG. 3D :
  • y n represents the 2 N output samples that result from the individual inverse transform prior to overlapping, adding and discarding as appropriate for the designed windows. It should be noted, that this transform has an efficient implementation as a block multiply and FFT.
  • the samples y n are added to a set of samples remaining from previous transform(s) in what is known as an overlap and add method. It should be evident to someone skilled in the art that this process of overlapping and combining is dependent on the frame size, transform size and window functions, and should be designed to achieve a accurate reconstruction of the input signal in the absence of any processing or modification of the signal, X n , in the frequency domain.
  • x n and X n in the above expressions of transform is for convenience.
  • the transform can be run more often or "oversampled.”
  • this window extends over the complete range of 2N samples.
  • STFT short term Fourier transform
  • the analysis and synthesis windows of FIG. 3A and FIG. 3D can be of length greater or smaller than the examples given herein.
  • a smaller window can be represented in the general form suggested above with a set of zero coefficients (zero padding).
  • a longer window is typically implemented by applying the window and then folding the signal into the transform processing range of the 2N samples. It is known that the window design affects certain aspects of: frequency resolution, independence of the frequency domain bins, latency, and processing distortions.
  • a general property which is achieved or approximated by a suitable window is that after the application of the input and output windows, and overlapping after an interval M, a constant gain is achieved without modulation over time across the M sample frame.
  • u n v n + u n + M v n + M k
  • the standard complex-valued fast Fourier transform can be used in implementing the transforms used herein, so that this complete transform has an efficient implementation using a set of complex block multiplication and a standard FFT. While not meant to be limiting, such that other embodiments can use other designs, this design facilitates porting of the transform or filterbank by taking advantage of any standard existing optimized FFT implementation for the target processor platform.
  • window and complex twist may be different for each of the inputs, e.g., microphone inputs to effect appropriate time delay to be used in the mixing down, e.g., beamforming and in the positional inference.
  • the window and complex twist may be different for each of the inputs, e.g., microphone inputs to effect appropriate time delay to be used in the mixing down, e.g., beamforming and in the positional inference.
  • the N complex-valued bins for each of the P inputs are used directly to create a set of positional estimates of spatial probability of activity. This is shown in FIG. 1 as banded spatial feature estimator 105 and in FIG. 2 as step 205. The details and operation of element 105 and step 205 are described in more detail below after a discussion of the downmixing, e.g., by beamforming.
  • the N complex-valued bins for each of the P inputs are combined to make a single frequency domain channel, e.g., using a downmixer, e.g., a beamformer 107.
  • a downmixer e.g., a beamformer 107.
  • the downmixer is a beamformer 107 designed to achieve some spatial selectivity towards the desired position.
  • the beamformer 107 is a linear time invariant process, i.e., a passive beamformer defined in general by a set of complex-valued frequency-dependent gains for each input channel. Longer time extent filtering may be included to create a selective temporal and spatial beamformer.
  • Possible beamforming structures include a real-valued gain and combination of the P signals, for example in the case of two microphones this might be a simple summation or difference.
  • the term beamforming as used herein means mixing-down, and may include some spatial selectivity.
  • the beamformer 107 (and beamforming step 207) can include adaptive tracking of the spatial selectivity over time, in which case the beamformer gains (also called beamformer weights) are updated as appropriate to track some spatial selectivity in the estimated position of the source of interest.
  • the tracking is sufficiently slow such that the time varying process beamformer 107 can be considered static for time periods of interest. Hence, for simplicity, and for analysis of the short-term system performance, it is sufficient to assume this component is time invariant.
  • the downmixer e.g., beamformer 107 and step 207 include using complex-valued frequency-dependent gains (mixing coefficients) derived for each processing bin.
  • Such a filter may be designed to achieve a certain directivity that is relatively constant or suitably controlled across different frequencies.
  • the downmixer, e.g., beamformer 107 will be designed or adapted to achieve an improvement in the signal to noise ratio of the desired signal, relative to that which would be achieved by any one microphone input signal.
  • beamforming is a well-studied problem and there are many techniques for achieving a suitable beamformer or linear microphone array process to create the mixed-down, e.g., beamformed signal out of beamformer 107 and step 207.
  • the beamforming 207 by beamformer 107 includes the nulling or cancellation of specific signals arriving from one or more known locations of sources undesired signal, such as echo, noise, or other undesired signal. While “nulling” suggest reducing to zero, in this description, “nulling” means reducing the sensitivity; those skilled in the art would understand that "perfect” nulling is not typically achievable in practice. Furthermore, the linear process of the beamformer is only able to null a small number (P-1) of independently located sources. This limitation of the linear beamformer is complemented by the more effective spatial suppression described later as a part of some embodiments of the present invention. The location of spatial response of the microphone array to the expected dominant echo path may be known and relatively constant.
  • the source of the echo would be known as coming from the speaker(s).
  • the beamformer is designed to null, i.e., provide zero or low relative sensitivity to sound arriving from the known location of source(s) of undesired signal.
  • Embodiments of the present invention can be used in a system or method that includes adaptive tracking of the spatial selectivity over time, e.g., using a beamformer 107 that can be updated as appropriate to track some spatial selectivity in the estimated position of the source of interest. Because such tracking is typically a fairly slow time varying process compared to the time T , for analysis of the system performance it is sufficient to assume each of the beamformer 107 and beamforming 207 is time invariant.
  • one embodiment uses for beamformer 107 a passive beamformer 107 that determines the simple sum of the two input channels.
  • beamforming 207 includes introducing a relative delay and differencing of the two input signals from the microphones. This substantially approximates a hypercardioid microphone directionality pattern.
  • the designed mixing of the P microphone inputs to achieve a single intermediary signal has a preferential sensitivity for the desired source.
  • the downmixer e.g., the beamforming 207 of beamformer 107 weights the sets of inputs (as frequency bins) by a set of complex valued weights.
  • the beamforming weights of beamformer 107 are determined according to maximum-ratio combining (MRC).
  • MRC maximum-ratio combining
  • the beamformer 107 uses weights determined using zero-forcing. Such methods are well known in the art.
  • the mixed-down e.g., beamformed signal from the microphone array
  • the transformed signal resulting from the combination of all of the echo reference inputs.
  • each frequency bin contains a contribution from more than one or more frequency bins, with at least 90% of the bands having contributions from two or more bins, the number of bins non-decreasing with frequency such that higher frequency bands have contribution from more bins than lower frequency bands.
  • FIG. 3B shows the conversion of the N bins to a number B of frequency bands carried out by banding elements 109 and 115, and banding steps. 209 and 217.
  • One aspect of the invention is the determination of a set of B suppression gains for the B bands. The determination of the gains incorporates statistical spatial information.
  • the raw frequency domain representation data is required for the intermediate signal, as this will be used in the signal synthesis to the time domain, the raw frequency domain coefficients of the echo reference are not required and can be discarded after calculating the power spectra (or other amplitude metric spectra). As described previously, the full set of P frequency domain representations of the microphone inputs is required to infer the spatial properties of the incident audio signal.
  • the B bands are centered at frequencies whose separation is monotonically non-decreasing.
  • the band separation is monotonically increasing in a log-like manner. Such a log-like manner is perceptually motivated.
  • they are on a psycho-acoustic scale, that is, the frequency bands are critically spaced, or follow a spacing related by a scale factor to critical spacing.
  • the banding of elements 109 and 115, and steps 209 and 217 is designed to simulate the frequency response at a particular location along the basilar membrane in the inner ear of a human.
  • the banding 109, 115, 209, 217 may include a set of linear filters whose bandwidth and spacing are constant on the Equivalent Rectangular Bandwidth (ERB) frequency scale, as defined by Moore, Glasberg and Baer ( B. C. J. Moore, B. Glasberg, T. Baer, "A Model for the Prediction of Thresholds, Loudness, and Partial Loudness," J. of the Audio Engineering Society (AES), Volume 45 Issue 4 pp. 224-240; April 1997 ).
  • ERP Equivalent Rectangular Bandwidth
  • Bark frequency scale may be employed with reduced performance.
  • each of the single channels obtained for the mixed-down, e.g., beamformed input signals and for the reference input is reduced to a set of B spectral power (or other frequency domain amplitude metric), e.g., B such values on a psycho-acoustic scale.
  • B spectral power or other frequency domain amplitude metric
  • the B bands can be fairly equally spaced on a logarithmic frequency scale. All such log-like banding is called "perceptual banding" herein
  • each band should have an effective bandwidth of around 0.5 to 2 ERB with one specific embodiment using a bandwidth of 0.7 ERB.
  • each band has an effective bandwidth of 0.25 to 1 Bark.
  • One specific embodiment uses a bandwidth of 0.5 Bark.
  • the inventors found it useful to keep the minimum band size to cover several frequency bins, as this avoids problems of temporal aliasing and circulant distortion in both time to frequency band-analysis-and frequency-to-time-synthesis-that can occur with transforms such as the short time Fourier transform. It is noted that certain transforms or subbanded filter banks such as the complex quadrature mirror filter, can avoid many of these issues.
  • the inventors found it advantageous that the characteristic shape and overlap of the banding used for power (or other frequency domain amplitude metric) representation and gain interpolation be relatively smooth.
  • the audio was high-pass filtered with a pass-band starting at around 100Hz. Below this, it was observed that the input, e.g., microphone signals are typically very noisy with a poor signal-to-noise ratio and it becomes increasingly difficult to achieve a perceptual spacing on account of the fixed length N transform.
  • This particular perceptual banding for elements 109, 115 and steps 209, 217 is suggestive and not meant to limit the invention to such banding.
  • the banding 109, 115 and steps 209, 217 need not be logarithmic or log-like.
  • the logarithmic banding is suggested and effective. The logarithmic banding approach significantly reduces complexity and stabilizes the power estimation and associated processing that occur at higher frequencies.
  • the banding of elements 109, 115 and steps 209, 217 can be achieved with a soft overlap using banding filters, the set of banding filters also called an analysis filterbank.
  • the shape of each banding filter should be designed to minimize the time extent of the time domain filters associated with each band.
  • the banding operation of elements 109, 115 and steps 209, 217 can be represented by a B * N real-valued matrix taking the bin power (or other frequency domain amplitude metric) to the banded power (or other frequency domain amplitude metric). While not necessary, this matrix can be restricted to positive values as this avoids the problem of any negative band powers (or other frequency domain amplitude metric).
  • this matrix should be fairly sparse with bands only dependent on the bins around their center frequency.
  • An optimal filter shape for achieving the compact form in both the frequency and time domain would be a Gaussian.
  • An alternative with the same quadratic main lobe but a faster truncation to zero is a raised cosine. With each band extending to the center of the adjacent bands, the raised cosine also provides a unity gain when the bands are summed. Since the raised cosine becomes sharp for the smaller bands, it is advisable to also include an additional spreading kernel such as [1 2 1]/4 or [1 4 6 4 1]/16 across the frequency bins. This has negligible effect on the wider bands at higher frequency however it provides a softening and thus limits the time spread of the associated band filters at lower frequencies.
  • this matrix is used to sum the powers (or other frequency domain amplitude metric) from the N bins into the B bands.
  • the transform of this matrix is used to interpolate the B suppression gains into a set of N gains to apply to the transform bins.
  • FIG. 5 depicts example shapes of the B bands in the frequency domain on both a linear and logarithmic scale. It can be seen that the B bands are approximately evenly spaced on the logarithmic scale with the lower bands becoming slightly wider. The term log-like is used for such behavior. Also shown in the FIG. 5 is the sum of example band filters. It can be seen that this has a unity gain across the spectrum with a high pass characteristic having a cut-off frequency around 100Hz.
  • the high frequency shelf and banding are not essential components of the embodiments presented herein, but are suggested features for use on typical microphone input signals for the case of the signal of interest being a voice input.
  • FIG. 6 shows time domain filter representations for several of the filter bands of example embodiments of banding elements 109, 115 and steps 209, 217.
  • an additional smoothing kernel [1 2 1]/4 is applied in the construction of the banding matrix coefficients. It can be seen that the filter extent is constrained to the center half of the time window around time zero. This property results by having the filter bands being wider than a single bin and, in this example, the additional smoothing kernel used in the determination of the banding matrix.
  • some embodiments include scaling the power (or other metric of the amplitude) in each band to achieve some nominal absolute reference. This has been found useful for suppression in order to facilitate suppression of residual noise to a constant power across frequency value relative to the hearing threshold.
  • One suggested approach for normalization of the bands is to scale such that the 1kHz band has unity energy gain from the input, and the other bands are scaled such that a noise source having a relative spectrum matching the threshold of hearing would be white or constant power across the bands. In some sense, this is a pre-emphasis filter on the bands prior to analysis which causes a drop in sensitivity in the lower and higher bands.
  • FIG. 7 shows the normalization gain for the banding to 30 bands as described above. Note that the 1kHz band is band 13 and thus has the 0dB gain.
  • Y n the frequency bins of the mixed-down, e.g., beamformed signal (combined with noise and echo) of the most recent T-long frame (the current frame) of M samples.
  • Y b ' is the banded instantaneous power of the mixed-down, e.g., beamformed signal
  • W b is the normalization gain from FIG. 7
  • w b,n are the elements from the banding matrix shown in FIGS. 4 and 5 .
  • spectral banding element 115 forms X b ' , the banded instantaneous power of the combined reference signal, using the W b normalization gain and a banding matrix with elements w b,n .
  • useful metric is obtained by combining the weighted amplitudes across the bins used in a particular band, with exponent p , and then applying a further exponent of 1 / q.
  • each band has a different metric.
  • the goal of the method embodiments and system embodiments includes determining an estimate for the various components of the banded mixed-down audio signal that are included in the total power spectrum (or other amplitude metric spectrum) in that band. These are determined as power spectra (or other amplitude metric spectra). Determination of the components in a frequency band of the beamformed signal Y b ' is described below in more detail.
  • spatial probability indicators determined by banded spatial feature estimator 105 in step 205, are used to spatially separate a signal into the components originating from the desired location and those not.
  • the estimations of the spatial probability indicators, and of the components of the overall signal spectra are interrelated.
  • FIG. 8A and FIG. 8B show two decompositions of the signal power (or other frequency domain amplitude metric) in a band.
  • FIG. 8A shows a separation of the echo power and noise power from power spectrum estimate of the mixed-down, e.g., beamformed signal to residual signal power, and further a separation into the desired in-position signal as a fraction of the residual signal power.
  • FIG. 8B shows a spatial of the total power in a band b into the total in-position power, and the total out-of-position power, and a separation of the total in-position power to an estimate of the desired signal power without an in-position echo power component and an in-position noise power component from the in-position power.
  • Embodiments of the present invention use the available information used to create some bounds for the estimate of the power in the desired signal, and create a set of band gains accordingly that can be used to affect simultaneous combined suppression.
  • signal power (or other frequency domain amplitude metric) estimator 121 generates an estimate of the total signal power (or other metric of amplitude) in each band b.
  • Embodiments of the present invention include determining in element 121, step 211 the overall signal power spectra (or other amplitude metric spectra) and noise power spectra (or other amplitude metric spectra). This is carried out on the mixed-down, e.g., beamformed instantaneous signal power Y b ' .
  • the downmixing e.g., beamforming 207 is a linear and time invariant process for the duration of interest
  • the mapping of the statistic of the noise and echo from the inputs X p,n to the output of the downmixer, e.g., beamformer 107, and ultimately its banded version Y b ' are also time invariant for the duration of interest.
  • the initial beamformer is a linear and time invariant process over the time of observation used for the estimation of statistics, e.g., the power spectra, and thus the nature of the estimates relative to the underlying signal conditions prior to the beamforming are not changing due to rapid adaption of the beamformer with the signal conditions.
  • the variance of such an estimate depends on the length of time over which the signal is observed. For longer transform blocks, e.g., N >512 at 16kHz, the immediate band power (or other frequency domain amplitude metric) suffices. For shorter transform blocks N ⁇ 512 at 16kHz, some additional smoothing or averaging is preferred, although not necessary.
  • one embodiment determines the power estimate P b ' using a first order filter to smooth the signal power (or other frequency domain amplitude metric) estimate. In one embodiment, P b ' .
  • the offset Y min ' is added to avoid a zero level power spectrum (or other amplitude metric spectrum) estimate.
  • Y min ' can be measured, or can be selected based on a priori knowledge.
  • Y min ' for example, can be related to the threshold of hearing or the device noise threshold.
  • the instantaneous power (or other frequency domain amplitude metric) Y b ' is a sufficiently accurate estimate of the signal power (or other frequency domain amplitude metric) spectrum Y b ' , such that element 121 is not used, but Y b ' is used for P b ' .
  • the banding filters and the frequency bands are chosen according to criteria based on psycho-acoustics, e.g., with the log-like banding as described above. Therefore, in the formulae presented herein in which P b ' is used, some embodiments use Y b ' instead.
  • Method 200 includes step 221 of performing prediction of the echo using adaptively determined echo filter coefficients (see echo spectral prediction filter 117), performing noise spectral estimation using the predicted echo spectral content and the total signal power (see noise estimator 123), updating the voice-activity echo detector (VAD) using the signal spectral content, noise spectral content, and echo spectral content (see element 125), and adapting the echo filter coefficients based on the VAD output and the signal spectral content, noise spectral content, and echo spectral content (see adaptive filter updater 127 that updates the coefficients of filter 117).
  • VAD voice-activity echo detector
  • the echoes are created at the microphones due to the acoustic reproduction of signals related to the one or more reference signals.
  • the potential source of echoes are typically rendered, e.g., via a set of one or more loudspeakers.
  • a summer 111 is used to determine a direct sum of the Q rendered reference signals to generate a total reference to be used for echo spectral content prediction for suppression.
  • such a sum or grouped echo reference may be obtained by a single non-directional microphone having a much greater level of echo and lower level of the desired signal compared to the signals of input microphones.
  • the signals are available in pre-rendering form.
  • the digital signals that are converted to analog then rendered to a set of one or more loudspeakers may be available.
  • the analog speaker signals may be available.
  • the electronic signals, analog or digital are used, and directly summed by a summer 111, in the digital or analog domain to provide M-sample frames of a single real-valued reference signal. The inventors have found that using the signals pre-rendering provides advantages.
  • Step 213 of method 200 includes the accepting (and summing) of the Q reference signals.
  • Step 215 includes transforming the total reference into frequency bins, e.g., using a time-to-frequency transformer 113 or a processor running transform method instructions.
  • Step 217 includes banding to form B spectral bands of the transformed reference, e.g., using a spectral bander 115 to generate the transform instantaneous power or other metric denoted Y b ' . This is used to predict the echo spectral content using an adaptive filter.
  • the adaptive filter to predict the echo power spectra (or other amplitude metric spectra) bands.
  • Those in the art will be familiar with adaptive filter theory. See for example, Haykin, S., Adaptive Filter Theory Fourth ed. 2001, New Jersey: Prentice Hall .
  • adaptive filters are applied in embodiments of the present invention, there may be some complications on account of the banded power spectra (or other amplitude metric spectra) being a positive real-valued signal and thus not zero mean.
  • an additional and sensible constraint is made for the power spectra (or other amplitude metric spectra) prediction by restricting the adaptive filter coefficients to be positive.
  • These filter coefficients are determined by an adaptive filter coefficient updater 127.
  • the filter coefficients require initialization, and in one embodiment, the coefficients are initialized to 0, and in another, they are initialized to an a priori estimate of the expected echo path.
  • One option is to initialize the coefficients to produce an initial echo power estimate that has a relatively high value - larger than any expected echo path which facilitates an aggressive starting position for echo and avoids the problem of an underestimated echo triggering the VAD and preventing adaption.
  • Adaptively updating the L filter coefficients uses the signal power (or other frequency domain amplitude metric) spectrum estimate P b ' from the current time frame and the noise power (or other frequency domain amplitude metric) spectrum estimate N b ' from the current time frame.
  • Y b ' is a reasonably good estimate of P b ' , so is used for determining the L filter coefficients rather than P b ' (which in any case is determined from Y b ' ).
  • One embodiment includes time smoothing of the instantaneous echo from echo prediction filter 117 to determine the echo spectral estimate E b ' .
  • the time constant in one embodiment is not frequency-band-dependent, and in other embodiments is frequency-band dependent. Any value between 0 and 200ms could work. A suggestion for such time constants ranges from 0 to 200ms and in one embodiment the inventors used values of 15 to 200 ms as a frequency-dependent time constant embodiments, whilst in another a non-frequency-dependent value of 30ms was used.
  • Different embodiments of the present invention can use different noise estimation methods, and the inventors have found a leaky minimum follower to be particularly effective.
  • a simple noise estimation algorithm can provide appropriate performance.
  • One example of such an algorithm is the minimum statistic. See R. Martin, "Spectral Subtraction Based on Minimum Statistics," in Proc. Euro. Signal Processing Conf. (EUSIPCO), 1994, pp. 1182-1185 .
  • Using the minimum statistic (a minimum follower) is appropriate, e.g., when the signal of interest has high flux and drops to zero power in any band of interest reasonably often, as is the case with voice.
  • one embodiment of the invention includes echo-gated noise estimation: updating the noise estimate N b ' , and stopping the update of the noise estimate when the predicted echo level is significant compared with the previous noise estimate. That is, that noise estimator 123 provides an estimate which is gated when the predicted echo spectral content is significant compared to the previously estimated noise spectral content.
  • a simple minimum follower based on a historical window can be improved.
  • the estimate from such a simple minimum follower can jump suddenly as extreme values of the power enter and exit the historical window.
  • the simple minimum follower approach also consumes significant memory for the historical values of signal power in each band.
  • some embodiments of the present invention use a "leaky” minimum follower with a tracking rate defined by at least one minimum follower leak rate parameter.
  • the "leaky” minimum follower has exponential tracking defined by one minimum follower rate parameter.
  • the parameter ⁇ N,b is best expressed in terms of the rate over time at which minimum follower will track. That rate can be expressed in dB/sec, which then provides a mechanism for determining the value of ⁇ N,b .
  • the range is 1 to 30dB/sec. In one embodiment, a value of 20dB/sec is used.
  • the one or more leak rate parameters of the minimum follower are controlled by the probability of voice being present as determined by voice activity detecting (VAD). If the probability of voice suggests there is a higher probability of voice being present, the leakage is a bit slower, and if there is probability there is not voice, one leaks faster. In one embodiment, a rate of 10dB/sec is used when there is voice detected, whilst a value of 20dB/sec is used otherwise.
  • VAD voice activity detecting
  • VADs may be used, and as described in more detail further in this description, one aspect of the invention is the inclusion of a plurality of VADs, each controlled by a small set of tuning parameters that separately control sensitivity and selectivity, including spatial selectivity, such parameters tuned according to the suppression elements in which the VAD is used in.
  • VAD Voice activity detector
  • VAD or voice activity detector is used loosely herein.
  • the measure S is a measure indicative of the number of bands that have a signal (indicated by Y b ' ) that exceeds the present estimate of noise and echo by pre-defined amounts, indicated by ⁇ N , ⁇ B > 1. Since the noise estimate is an estimate of the stationary or constant noise power (or other frequency domain amplitude metric) in each band, rather than being a true "voice" activity measure, the measure S is a measure of transient or short time signal flux above the expected noise and echo.
  • the VAD derived in the echo update voice-activity detector 125 and filter updater 127 serves the specific purpose of controlling the adaptation of the echo prediction.
  • a VAD or detector with this purpose is often referred to as a double talk detector.
  • the values of ⁇ N , ⁇ E . are between 1 and 4. In a particular embodiment, ⁇ N , ⁇ E are each 2.
  • Y' sens is set to be around expected microphone and system noise level, obtained by experiments on typical components. Alternatively, one can use the threshold of hearing to determine a value for Y sens .
  • Voice activity is detected, e.g., to determine whether or not to update the prediction filter coefficients in echo prediction filter coefficient adapter 127, by a threshold, denoted S thresh in the value of S.
  • S thresh a threshold
  • a continuous variation in the rate of adaption may be effected with respect to S
  • the operation in the echo update voice activity detector 125 has been found to be a simple yet effective method for voice or local signal activity detection. Since ⁇ N > 1 and ⁇ E > 1, each band must have some immediate signal content greater than the estimate of noise and echo. Typical values for ⁇ N , ⁇ E are around 2. With the suggested values of ⁇ N , ⁇ E of around 2, a signal to noise ratio of at least 3dB is required for a contribution to the signal level parameter S. If the current signal level is large relative to the noise and echo estimate, the summation term has a maximum of 1 for each band. The sensitivity offset in the denominator of the expression for S prevents S and thus any derived activity detector, such as the VAD 125, from registering at low signal levels.
  • the suggested scaling related to band size and threshold of hearing creates an effective balancing of the VAD expression with each band having a similar sensitivity and perceptually weighted contribution without tuning VAD parameters separately for each band.
  • Echo prediction filter coefficient adapter gated by an activity threshold
  • the echo filter coefficient updating of updater 127 is gated, with updating occurring when the expected echo is significant compared to the expected noise and current input power, as determined by the VAD 125 and indicated by a low value of local signal activity S.
  • ⁇ N is a tuning parameter tuned to ensure stability between the noise and echo estimate.
  • a typical value for ⁇ N is 1.4 (+3dB).
  • ⁇ is a tuning parameter that affects the rate of convergence and stability of the echo estimate. Values between 0 and 1 might be useful in different embodiments.
  • ⁇ 0.1 independent of the frame size
  • X sens ' is set to avoid unstable adaptation for small reference signals.
  • X sens ' is related to the threshold of hearing.
  • X sens ' is a pre-selected number of dB lower than the reference signal, so is set relative to the expected power (or other frequency domain amplitude metric) of the reference signal, e.g., 30 to 60 dB below the expected power (or other frequency domain amplitude metric) of X b ' in the reference signal.
  • S thresh it is 30dB below the expected power (or other frequency domain amplitude metric) in the reference signal.
  • the choice of value for S thresh depends on the number of bands. S thresh is between 1 and B, and for one embodiment having 24 bands to 8kHz, a suitable range was found to be between 2 and 8, with a particular embodiment using a value of 4.
  • a band-dependent weighting factor can be introduced into the echo update voice-activity detector 125 such that the individual band contributions based on the instantaneous signal to noise ratio are weighted across frequency for their contribution to the detection of signal activity.
  • perceptual-based e.g., log-like banding
  • the inventors have found it acceptable to have a uniform weighting.
  • a band-dependent weighting function can be introduced.
  • the approach presented here for VAD-based echo filter updating is a very low complexity but effective approach for controlling the adaption and predicting the echo level.
  • the approach was also found to be fairly effective at avoiding bias in the noise and echo estimates caused by the potentially ambiguous joint estimation.
  • the proposed approach effectively deals with the interaction between the noise and the echo estimates and has been found to be robust and effective in a wide range of applications.
  • the approach is somewhat unconventional, in that the noise estimation method and echo prediction methods may not be the most accepted and established methods known, the approach was found to work well, and allows simple but robust techniques to be used in a systematic way to effectively reduce and control any error or bias.
  • the invention is not limited to the particular noise estimation method used or to the particular echo prediction method used.
  • a solution to this problem is to force adaption initially or repeatedly when some reference signal commences, or initialize the echo filter to be the expected of upper bound of the expected echo path.
  • the echo power spectrum (or other amplitude metric spectrum) is estimated, and this estimate has a resolution in time and frequency as set out by the transform and banding.
  • the echo reference need only be as accurate and have a similar resolution to this representation. This provides some flexibility in the mixing of the Q reference inputs as discussed above.
  • the inventors also found that there is also a toleration of gain variation of around 3-6dB due to the suppression rule and suggested values of the echo estimate scaling used in the VAD and suppression formulae.
  • Some embodiments of the invention do not include echo suppression, only simultaneous suppression of noise and out-of-location signals.
  • the elements involved in generating the echo estimate might not be present, including the reference inputs, elements 111, 113, 115, filter 117, echo update VAD 125 and element 127.
  • steps 213, 215, 217, and 221 would not be needed, and step 223 would not involve echo suppression.
  • One aspect of embodiments of the invention is using the input signal data, e.g., input microphone data in the frequency or transform domain from input transformers 103 and transforming step 203 to form estimates of the spatial properties of the sound in each band. This is sometimes referred to as inferring the source direction or location.
  • the presence of near-field objects means that the spatial location of an object can only be expressed in terms of the expected signal properties at the array of sound arriving from that desired or other source.
  • the source position location is not determined, but rather characteristics of the incident audio in terms of a set of signal statistics and properties are determined as a measure of the probability of a source of sound being or not being at a particular location.
  • Embodiments of the present invention include estimating or determining banded spatial features, carried out in the system 100 by banded spatial feature estimator 105, and in method 200 by step 205. Some embodiments of the present invention use an indicator of the probability of the energy in a particular band b having originated from a spatial region of interest. If, for example, there is a high probability in several bands, it is reasonable to infer that is it from a spatial region of interest.
  • Embodiments of the present invention use spatial information in the form of one or more measures determined from one or more spatial features in a band b that are monotonic with the probability that the particular band b has such energy incident from a spatial region of interest. Such quantities are called spatial probability indicators.
  • the P sets of N complex values after the microphone input transforms are routed to a processing element for banded positional estimation.
  • the relative phase and amplitudes of the input microphones in each transform bin can be used to infer some positional information about the dominant source in that frequency bin for the given processing instant. With a single observation of a bin at that processing instant, it is possible to resolve the direction or position of at most P - 1 sources, assuming that we know the number of sources. See, for example, Wax, M. and I. Ziskind, On unique localization of multiple sources by passive sensor arrays. IEEE Trans. Acoustics, Speech, and Signal Processing, vol. 37, no. 7, pp. 996-1000, 1989 .
  • an estimate is made of a measure monotonic with the probability that energy in a given band at that point time could reasonably have arrived from the desired location, which is represented by a target position in the array manifold.
  • the target position in the array manifold may be based on a priori information and estimates, or it may take advantage of previous online estimates and tracking (or a combination of both).
  • the result of the spatial inference is to create an estimate for a measure of probability, e.g., as an estimated fraction or as an appropriate gain that relates to the estimated amount of signal from the desired location, in that band at that point in time.
  • one or more spatial probability indicators are determined in step 205 by banded spatial feature estimator 105, and used for suppression. These one or more spatial probability indicators are one or more measures in a band b that are monotonic with the probability that the particular band b has such energy in a region of interest.
  • the spatial probability indicators are functions of one or more weighted banded covariance matrices of the inputs.
  • the one or more spatial probability indicators are functions of one or more banded weighted covariance matrices of the input signals.
  • the w b,n provide an indication of how each bin is weighted for contribution to the bands. This creates an estimate of the instantaneous array covariance matrix at a given time and frequency instant. In general, with multi-bin banding, each band contains a contribution from several bins, with the higher frequency bands having more bins. This use of banded covariance has been found to provide a stable estimate of the covariance, such covariance being weighted to the signal content having the most energy.
  • the one or more covariance matrices are smoothed over time.
  • the smoothing is defined by a frequency dependent time constant R ⁇ b :
  • R b ′ ⁇ R b R b ′ + 1 ⁇ ⁇ R b R b Pr ev ′ .
  • R b Pr ev ' is a previously determined covariance matrix.
  • the spatial features include a "ratio" spatial feature, a "phase” spatial feature, and a “coherence” spatial feature. These features are used to determine an out-of-location signal probability indicator, expressed as a suppression gain, and determined using two or more of the spatial features, and a spatially-selective estimate of noise spectral content determined using two or more of the spatial features.
  • the three spatial features ratio, phase, and coherence are used, and how to modify these embodiments to include only two of the spatial features would be straightforward to one of ordinary skill in the art.
  • ratio a quantity that is monotonic with the ratio of the banded magnitudes R b 11 ′ R b 22 ′ .
  • it is the determined, or estimated (a priori) value of the noise power (or other frequency domain amplitude metric) in band b for the microphone and related electronics. That is, the minimum sensitivity of any preprocessing used.
  • Phase ′ b tan ⁇ 1 R b 21 ′ .
  • the spatial feature Denote by the spatial feature "coherence” a quantity that is monotonic with R b 21 ′ R b 12 ′ R b 11 ′ R b 22 ′ .
  • related measures of coherence could be used such as 2 R b 21 ′ R b 12 ′ R b 11 ′ R b 11 ′ + R b 22 ′ R b 21 ′ or values related to the conditioning, rank or eigenvalue spread of the covariance matrix.
  • alternate embodiments may use a logarithmic scale in dB, such as Coherence ′ b
  • dB 5 log 10 R b 21 ′ R b 12 ′ + ⁇ 2 R b 11 ′ R b 22 ′ + ⁇ 2 .
  • FIG. 9A , 9B and 9C show the probability density functions over time of the spatial features Ratio' b , Phase' b , and Coherence' b , respectively, for diffuse noise, shown solid, and a desired signal, in this case voice, shown by dotted lines, as calculated for two inputs captured by a two-microphone headset with a microphone spacing of around 50mm across 32 frequency bands.
  • the incoming signals were sampled at a sampling rate of 8kHz, and the 32 bands are on an approximate perceptual scale with center frequencies from 66Hz to 3.8kHz.
  • the expected ranges are -10 to +10dB for Ratio' b , -180° to 180° for Phase' b , and 0 to 1 for Coherence' b .
  • the plots were obtained from around 10s of the noise and of the desired voice signal, with a frame time interval T of 16ms. As such, around 600 observations of the feature were accumulated for each distribution plot.
  • Plots such as shown in FIGS. 9A , 9B and 9C are useful for determining the design of the probability indicators, in that they represent the spread of feature values that would be expected for the desired and undesired signal content.
  • the noise field is diffuse and can be comprised of multiple sources arriving from different spatial locations.
  • the spatial features Ratio' b , Phase' b , and Coherence' b for the noise are characteristic of a diffuse or spatially random field.
  • the noise is assumed to be in the farfield whilst the desired signal-the voice- is in the nearfield, however this is not a requirement for the application of this method.
  • the microphones were matched such that the average ratio feature for the noise field is 0dB, i.e., a ratio of 1.
  • Noise signals arrive at the two microphones with a relatively constant expected power. For low frequencies the microphone signals would be expected to be correlated due to the longer acoustic wavelength, and the ratio feature for noise is concentrated around 0dB.
  • the acoustic signal at the microphones can become independent in a diffuse field, and thus a spread in the probability density function of the ratio feature for noise is observed with higher frequency bands.
  • the phase spatial feature for the diffuse noise field is centered around 0°.
  • the characteristic of the head and device design create a deviation from the theoretical spaced microphone diffuse field response.
  • the wavelength decreases relative to the microphone spacing and the ratio and phase features for the noise become more distributed as the microphones become independent in the diffuse field.
  • the distributions of the noise and desired signal show a degree of separation. From such distributions, one aspect of embodiments of the invention is to use an observation of each of these features in a given band to infer a partial probability of the incident signal being in the desired spatial location. These partial probabilities are referred to as spatial probability indicators herein.
  • spatial probability indicators In some bands the distributions of a spatial feature for voice and noise are disjoint, and therefore it would be possible to say with a high degree of certainty if the signal in that band is from the desired spatial location. However, there is generally some amount of overlap and thus the potential for noise to appear to have the desired statistical properties at the array, or for the desired signal to present a relationship at the microphone array that would normally be considered noise.
  • One feature of some embodiments of the invention is that, based on the a priori expected or current estimate of the desired signal features-the target values, e.g., representing spatial location, gathered from statistical data such as represented by the plots shown in FIGS. 9A-9C , or from a priori knowledge, each spatial feature in each band can be used to create a probability indicator for the feature for the band b.
  • One embodiment of the invention combines two or more of the probability indicators to form a combined single probability indicator used to determine a suppression gain, which, along with the additional information from noise and echo estimation, leads to a stable and effective combined suppression system and method.
  • the combining works to reduce the over processing and "musical" artifacts that would otherwise occur if each feature was used directly to apply a control or suppression to the signal. That is, one feature of embodiments of the invention is to make an effective combined inference or suppressive gain decision using all information, rather than to achieve a maximum suppression or discrimination from each feature independently.
  • the distributions of the expected spatial features for the desired location are modeled as a Gaussian distributions that present a robust way of capturing the region of interest for probability indicators derived from each spatial feature and band.
  • the function f R b ( ⁇ Ratio' ) is a smooth function.
  • the Width Ratio,b is related to but does not need to be determined from the actual data such as in FIG. 9A . It is set to cover the expected variation of the spatial feature in normal and noisy conditions, but also needs only be as narrow as is required in the context of the overall system to achieve the desired suppression.
  • Width Ratio,b is not necessarily obtained from data such as shown in FIG. 9A . In one embodiment, assuming a Gaussian shape, Width Ratio,b is 1 to 5dB which may vary with the band frequency.
  • Phase target b is determined from either prior estimates or experiments on the equipment used, e.g., headsets, obtained, e.g., from data such as shown in FIG. 9B .
  • the function f P b ( ⁇ Phase' ) is a smooth function.
  • f R b ⁇ Phas e b ′ exp ⁇ ⁇ Phas e b ′ Widt h Phase , b 2
  • Width Phase,b is a width tuning parameter expressed in units of phase.
  • Width Ph ⁇ se,b is related to but does not need to be determined from the actual data such as in FIG. 9B . It is set to cover the expected variation of the spatial feature in normal and noisy conditions, but also needs only be as narrow as is required in the context of the overall system to achieve the desired suppression. It typically needs to be tuned in the context of overall system performance.
  • the variance of the desired signal spatial features from sample data is a useful indication for the widths.
  • the spatial features are typically more stable, and therefore the widths could be narrow. Note however that too narrow a width may be overly aggressive, offering more suppressive ability than may be required at the expense of reduced voice or desired signal quality.
  • Matching the stability and selectivity of the spatial probability indicators is a process of tuning, guided by plots such as those of FIGS. 9A and 9B , to achieve the desired performance.
  • One consideration is the spread of the spatial feature resulting from a mixture of desired signal and noise.
  • the targets and widths for the ratio and phase features can be derived directly from data such as shown in FIG. 9A and 9B .
  • RP I b ′ exp ⁇ Rati o b ′ ⁇ Rati o target b WidthHig h Ratio , b 2 if Rati o b ′ > Rati o target b
  • RP I b ′ exp ⁇ Rati o b ′ ⁇ Rati o target b WidthLo w Ratio , b 2 if Rati o b ′ ⁇ Rati o target b .
  • PPI b WidthUp Ph ⁇ se , b and WidthDown Phase,b .
  • PP I b ′ exp ⁇ Phas e b ′ ⁇ Phas e target b WidthHig h Ratio , b 2 if Phas e b ′ > Phas e target b
  • PP I b ′ exp ⁇ Phas e b ′ ⁇ Phas e target b WidthLo w Phase , b 2 if Phas e b ′ ⁇ Phas e target b .
  • one embodiments includes determining pairwise spatial features and probability indicators for some or all pairs of signals. For example, for three microphones, there are three possible pairwise combinations. Therefore, for the case of determining the ratio, phase, and coherence spatial features, up to nine pairwise spatial features can be obtained, and probability indicators determined for each, and a combined spatial probability indicator determined for the configuration by combining two or more, up to nine spatial probability indicators.
  • One feature of embodiments of the invention is the use of statistical spatial information, e.g., the spatial probability indicators to determine suppression gains.
  • the determining of the gains is carried out by a gain calculator 129 in FIG. 1 and step 223 in method 200.
  • One set of B gains is the beam gain, a probability indicator used to determine a suppression probability indicator related to the probability of a signal coming from a source in the desired location or "in beam.” Similarly, related to this is a probability or gain for out-of-location signals, expressed in one embodiment as an out-of-beam gain.
  • the spatial probability indicators are used to determine what is referred to as the beam gain, a statistical quantity denoted BeamGain' b that can be used to estimate the in-beam and out-of-beam power from the total power, and further, can be used to determine the out-of-beam suppression gain.
  • the beam gain is the product of spatial probability indicators.
  • the probability indicators are scaled such that the beam gain has a maximum value of 1.
  • BeamGain ′ b BeamGai n min + 1 ⁇ BeamGai n min RPI ′ b ⁇ PPI ′ b ⁇ CPI ′ b .
  • Embodiments of the present invention use BeamGain min of 0.01 to 0.3 (-40dB to-10dB).
  • One embodiment uses a BeamGain min of 0.1.
  • While some embodiments of the invention use the product of all three spatial probability indicators as the beam gain, alternate embodiments use one or two of the indicators, i.e.,_in the general case, the beam gain is monotonic with the product of two or more of the spatial probability indicators.
  • the beam gain is used to determine the overall suppression gain as described herein below.
  • the beam gain is also used in some embodiments to estimate the in-beam power (or other frequency domain amplitude metric), that is, the power (or other frequency domain amplitude metric) in a given band b likely to be from the location of interest, and the out-of-beam power- the power (or other frequency domain amplitude metric) in a given band b likely to not be from the location of interest.
  • location or the general idea of a spatial position and mapping to a particular location on an array manifold, might be at a different angle of arrival, or might be nearfield vs. farfield, and so forth.
  • Y b the total banded power (or other frequency domain amplitude metric) from the mixed-down inputs, i.e., after beamforming.
  • BeamGain' b 2 can be 1,
  • Power ′ b , OutOfBeam 1 ⁇ BeamGain ′ b 2 Y b ′ .
  • Power' b,InBeam and Power' b,OutOfBeam are statistical measures used for suppression.
  • Embodiments of the present invention include determining an estimate of noise spectral content and using the estimate of noise spectral content to determine a noise suppression gain.
  • noise estimation noise is usually assumed to be stationary, whereas voice is assumed to have a high flux.
  • a spectrally monotonous voice signal might therefore be interpreted as noise, and should the suppression be based on such a noise estimate, there is a possibility that the voice will eventually be suppressed. It is desired to be less-sensitive to noise-like sounds that come from a location of interest.
  • a feature of some embodiments of the invention is use of the spatial probability indicators to improve the estimate noise power (or other frequency domain amplitude metric) spectral estimate for use to determine suppression gains taking location into account in order to reduce the sensitivity of suppression to noise-like sounds that come from a location of interest.
  • the noise suppression gain is based on a spatially-selective estimate of noise spectral content.
  • Another feature of some embodiments is the use of the spatial probability indicators to carry out spatially sensitive voice activity detection, which is used in carrying out suppression gains taking location into account.
  • FIG. 10 shows a simplified block diagram of an embodiment of the gain calculator 129 and includes a spatially-selective noise power (or other frequency domain amplitude metric) spectrum calculator 1005 that operates on an estimate of the out-of-beam power, denoted Power' OutOfBeam , generated by an out-of-beam power spectrum calculator 1003.
  • a spatially-selective noise power (or other frequency domain amplitude metric) spectrum calculator 1005 that operates on an estimate of the out-of-beam power, denoted Power' OutOfBeam , generated by an out-of-beam power spectrum calculator 1003.
  • FIG. 11 shows a flowchart of gain calculation step 223, and post-processing step 225 in embodiments that include post-processing, together with the optional step 226 of calculating and incorporating an additional echo gain.
  • the out-of-beam power spectrum calculator 1003 determines the beam gain BeamGain' b from the spatial probability indicators.
  • BeamGain ′ b BeamGain ′ min + 1 ⁇ BeamGai n min RP I b ⁇ PP I b ⁇ CP I b .
  • Each of element 1003 and step 1105 determines an estimate of the out-of-beam instantaneous power Power' b,OutOfBeam .
  • Power ′ b , OutOfBeam 1 ⁇ BeamGain ′ b 2 Y b ′ .
  • the out-of-beam banded spectral estimate and the out-of-beam banded spectral estimate are determined using the signal power (or other frequency domain amplitude metric) spectrum, P b ' , rather than Y b ' .
  • the inventors have found that Y b ' is a good approximation of P b ' .
  • Y b ' is more or less equal to P b ' , and it is not necessary to use the smoothed power estimate P b ' .
  • Each of spatially-selective noise power spectrum calculator 1005 and step 1107 determines an estimate of the noise power spectrum 1006 (or in other embodiments, the spectrum of another metric of the amplitude).
  • One embodiment of the invention uses a leaky minimum follower, with a tracking rate determined by at least one or leak rate parameter.
  • the leak rate parameter need not be the same as for the non-spatially selective noise estimation used in the echo coefficient updating.
  • N' b,S the spatially selective noise spectrum estimate 1006.
  • N b , S ′ min Powe r b , OutOfBeam ′ , 1 + ⁇ b N b , S Pr ev ′ , where N b , S Pr ev ' is the already determined, i.e., previous value of N' b,S .
  • the leak rate parameter ⁇ b is expressed in dB/s such that for a frame time denoted T, 1 + ⁇ b 1 T is between 1.2 and 4 if the probability of voice is low, and 1 if the probability of voice is high.
  • the at least one leak rate parameter of the leaky minimum follower used to determine N' b,S are controlled by the probability of voice being present as determined by voice activity detecting.
  • One aspect of the invention is simultaneously suppressing: 1) noise based on a spatially selective noise estimate and 2) out-of-beam signals.
  • Each of elements 1013 and step 1108 is shown in FIGS. 10 and 11 , respectively, with echo suppression, and in some versions does not include echo suppression.
  • Gai n N ′ max 0, Y b ′ ⁇ ⁇ N ′ N b , S Y b ′ GainExp
  • Y b ' is the instantaneous banded power (or other frequency domain amplitude metric)
  • N b is the banded spatially-selective (out of beam) noise estimate
  • ⁇ N ' is a scaling parameter, typically in the range of 1 to 4, to allow for error in the noise estimate and to offset the gain curve accordingly.
  • This scaling parameter is similar in purpose and magnitude to the constants used in the VAD function, though it is not necessarily equal to such a VAD scale factor.
  • the parameter GainExp is a control of the aggressiveness or rate of transition of the suppression gain from suppression to transmission. This exponent generally takes a value in the range of 0.25 to 4 with a preferred value in one embodiment being 2.
  • Some embodiments of the invention include not only noise suppression, but simultaneous suppression of echo. Thus, some embodiments of the invention include simultaneously suppressing: 1) noise based on a spatially selective noise estimate, 2) echoes, and 3) out-of-beam signals.
  • element 1013 includes echo suppression
  • step 1108 include echo suppression
  • the probability indicator 1014 for suppressing echoes is expressed as a gain denoted Gai n b , N + E ' .
  • echo suppression is included.
  • some embodiments of the invention do not include echo suppression, only simultaneous suppression of noise and out-of-location signals.
  • the elements involved in generating the echo estimate might not be present, including the reference inputs, elements 111, 113, 115, filter 117, echo update VAD 125 and element 127.
  • steps 213, 215, 217, and 221 would not be needed, and step 223 would not involve echo suppression.
  • Gain 1 for Gai n b , N + E ' applicable to simultaneous noise and echo suppression
  • MMSE minimum mean squared error
  • the present invention is broader, and in embodiments of the present invention, value of the GainExp b larger than 0.5 is found to be preferable in creating a transition region between suppression and transmission that is more removed from the region of expected noise power activity and variation.
  • the gain expressions achieve a relatively flat, or even inverted gain relationship with input power in the region of expected noise power - and the inventors consider this an inventive step in the design of the gain functions that significantly reduces instability of the suppression during noise activity.
  • One feature of some embodiments of the invention is significantly reducing this problem.
  • FIG. 12 shows a probability density in the form of a scaled histogram of signal power in a given band for the case of noise (solid line) and desired (voice) signal (broken line) in isolation obtained from observing around 10s of each signal class for a single band of around 1kHz where the noise and voice level correspond to an average signal to noise level of around 0dB.
  • the values are illustrative and not restrictive and it should be evident that this figure serves to capture the characteristics of the suppression gain calculation problem in order to demonstrate the desired properties and specific designs of some embodiments of such calculations.
  • the horizontal axes represent a scaled value of the instantaneous band power relative to the expected noise (and echo) power. This is effectively the ratio of input power to noise, which is related but slightly different to the more commonly used signal to noise ratio.
  • some lower limit must be placed on the noise and/or echo estimate such that the ratio of input signal power to noise remains bounded.
  • the value of this limit is not material, provided it is sufficiently small, since the probability indicators, expressed herein as gain functions, are asymptotically unity for large ratios of input power to expected noise.
  • the representation of gain vs. input power described herein is preferred to a more conventional representation in terms of gain vs. signal to noise ratio, as it better demonstrates the natural distribution of power in the different signal classes, and serves to highlight the design and benefits of using the gain expressions described herein.
  • expected noise and echo power is used to refer to the sum of the expected noise power and expected echo power at that time. At any specific time in a band, there could be either echo or noise or both signals present in any proportion.
  • the noise signal shows a spread of observed instantaneous input signal powers centered around the noise estimate and having an approximate range of ⁇ 10dB.
  • the desired signal in this case of voice, has a higher instantaneous power having a larger range and generally having an instantaneous power in the range of 5-20dB more than the noise when there is active voice.
  • the data was representative of an incident signal at the microphone where the ratio of the average voice signal and noise signal power was 0dB. However, since a voice signal is typically very non-stationary; the times and bands when speech is present show a higher signal level than the 0dB average would suggest.
  • any suppression gain should attenuate the noise components by a constant, and transmit the speech with unity gain.
  • the distributions of the desired signal and noise are not disjoint.
  • the design criteria for suppression used work to ensure relatively stable gain across the most probable speech levels and the most probable noise levels in order to avoid artifacts being introduced. To the inventor's knowledge, this is a new non-obvious inventive way of posing, visualizing and achieving a superior performing outcome for the suppression system.
  • Many prior art approaches are concerned with minimizing the numerical error in each bin or band against the original reference, which can lead to unstable gains and musical artifacts common in other solutions.
  • One feature of embodiments of the invention is the specification of the suppression gains for each band in the form of properties of the gain functions.
  • the constant or smooth gains across both the voice and noise power distribution modes ensures processing and musical noise musical artifacts are significantly reduced.
  • the inventors have found also that the methods presented herein can reduce the reliance on accurate estimates for the noise and echo levels.
  • the first uses a minimum threshold for the gain to prevent significant variation in gain around the expected noise/echo power, e.g., Gai n b , N + E ′ max 0.1, max 0, Y b ′ ⁇ ⁇ N ′ N b , S ′ ⁇ ⁇ E ′ E b ′ Y b GainEx p b where the minimum value selected, 0.1, is not meant to be limiting, and can be different in different embodiments. The inventors suggest a range of from 0.001 to 0.3 (-60dB to -10dB), and the minimum can be frequency dependent.
  • the minimum value selected, 0.1 is not meant to be limiting, and can be different in different embodiments.
  • the inventors suggest a range of from 0.001 to 0.3 (-60dB to -10dB), and the minimum can be frequency dependent.
  • the second value is sensibly 1 minus the first value.
  • GainExp' b is a parameter usable to control the aggressiveness of the transition from suppression to transmission and may take values ranging from 0.5 to 4 with a preferred value in one embodiment being 1.5.
  • the first two values, shown here as 0.1 and 0.01 are adjusted to achieve the required minimum gain value and transition period.
  • the minimum value shown, 0.1 is not meant to be limiting, and can be different in different embodiments.
  • the scalar 0.01 is set to achieve an attenuation of around 8dB with the input power at the expected noise and echo level. Again, different values can be used in different embodiments.
  • FIG. 13 shows the distribution of FIG. 12 , together with the gain expressions Gain 1, Gain 2, Gain 3, and Gain 4 described above as functions of the ratio of input power to noise.
  • the gain functions are shown plotted on a log scale in dB.
  • features of this family of suppression gain functions include, assuming that for each frequency band, a first range of values of banded instantaneous amplitude metric values is expected for noise, and a second range of values of banded instantaneous amplitude metric values is expected for a desired input:
  • This approach substantially reduces the degree of expansion that may occur due to excessive gradient or discontinuities in the gain as a function of the incoming banded signal power.
  • a gain whose curve has a negative gradient in at least some of the range of input powers expected for the noise signal.
  • lower power noise is attenuated less than higher power noise, which is a whitening process that reduces the dynamics of the noise over both frequency and time.
  • the extent to which such a negative slope is provided in the gain curve can be varied according to the circumstance.
  • the slope of the gain relative to the input power should not be lower than about -1 (in units of dB gain vs. dB input power).
  • the inventors also suggest that spikes and any sharp edges or discontinuities in the gain curve be avoided. It is also reasonable that the gain should not exceed unity. Therefore, the following is suggested for the noise and echo suppression gain:
  • a modified sigmoid function is used; the sigmoid function is modified by including an additional term to result in a desired negative gradient for input signal powers around the expected noise level.
  • a modified sigmoid function is used that includes a sigmoid function and an additional term to provide the negative gradient in the first region.
  • An expression is presented below for the modified sigmoid function that offers a similar level of suppression to the previous function suggested embodiment with the added property of achieving a significant reduction in the dynamic range of the noise. It is evident that there are computational simplifications for both the sigmoid function and the additional term.
  • FIG. 14 shows the histograms of FIG. 12 together with the sigmoid gain curve of Gain 4 and the modified sigmoid-like gain curve of Gain 5, called the whitening gain on the drawing.
  • Each of the plots has the input power to noise ratio in dB as the horizontal axis.
  • FIG. 15 shows what happens to the probability density functions, shown as scaled histograms, for the expected power of the noise for a noise signal and for a voice signal after applying the sigmoid-like gain curve Gain 4 and the whitening gain Gain 5.
  • each of these causes a significant increase in the separation of the voice and noise, with the noise level decreasing in power or shifting lower on the horizontal axis.
  • the first sigmoid gain, Gain 4 creates a spreading of the noise power. That is, the noise level fluctuates more in power than in the original noise signal. This effect may be worse for many prior art approaches to noise suppression that do not exhibit the smooth property of the sigmoid like functions through the main noise power distribution.
  • the voice levels are also slightly expanded.
  • the second modified sigmoid gain, Gain 5 has the property of compacting the noise power distribution. This makes the curve higher, since the central noise levels are now more probable. This means there are less fluctuations in the noise and a sort of smoothing or whitening which can lead to less intrusive noise.
  • both gain functions increase the signal to noise ratio by increasing the spread-reducing the noise levels.
  • the noise is less intrusive and partially whitened over time and frequency.
  • the suppression gain expressions above can be generalized as functions on the domain of the ratio of the instantaneous input power to the expected undesirable signal power, sometimes called "noise" for simplicity.
  • the undesirable signal power is the sum of the estimated (location-sensitive) noise power and predicted or estimated echo power. Combining the noise and echo together in this way provides a single probability indicator in the form of a suppressive gain that causes simultaneous attenuation of both undesirable noise and of undesirable echo.
  • an additional scaling of the probability indicator or gain is used, such additional scaling based on the ratio of input signal to echo power alone.
  • f A ( ⁇ ), f B ( ⁇ ) a pair of suppression gain functions, each having desired properties for suppression gains, e.g., as described above, including, for example being smooth.
  • each of f A ( ⁇ ) , f B ( ⁇ ) has sigmoid function characteristics.
  • f A Y b ′ N b , S ′ + E b ′ one can instead use a pair of probability indicators, e.g., gains f A Y b ′ N b , S ′ , f B Y b ′ E b ′ and determine a combined gain factor from f A Y b ′ N b , S ′ and f B Y b ′ E b ′ , which allows for independent control of the aggressiveness and depth for the response to noise and echo signal power.
  • f A Y b ′ N b , S ′ + E b ′ can be applied for both noise and echo suppression
  • f B Y b ′ E b ′ can be applied for additional echo suppression.
  • the two functions f A Y b ′ N b , S ′ , f B Y b ′ E b ′ are combined as a product to achieve a combined probability indicator, as a suppression gain.
  • the suppression probability indicator for in-beam signals expressed as a beam gain 1012, called the spatial suppression gain, and denoted Gain' b
  • S is determined by a spatial suppression gain calculator 1011 in element 129 ( FIG. 10 ) and by a calculating suppression gain step 1103 in step 223 as Gai n b
  • the spatial suppression gain 1012 is combined with other suppression gains in gain combiner 1015 and combining step 1109 to form an overall probability indicator expressed as a suppression gain.
  • additional smoothing is applied.
  • Gai n b , RAW ′ 0.1 + 0.9 Gai n b , S ′ ⁇ Gai n b , N + E ′ .
  • the softening is to ensure that at every point at which a parameter and an estimate is calculated, efforts are taken to ensure continuity and stability over time, signal conditions, and spatial uncertainly. This avoids any sharp edges or sudden relative changes in the gains that are typical as the probability indicator or gain becomes small.
  • Gain' b,RAW suppresses noise and echo equally. As discussed above, it may be desirable to not eliminate noise completely, but to completely eliminate echo.
  • Gai n b , RAW ′ 0.1 + 0.9 Gai n b , S ′ ⁇ f A Y b ′ N b , S ′ + E b ′ ⁇ f B Y b ′ E b ′ , where f A Y b ′ N b , S ′ + E b ′ achieves (relatively) modest suppression of both noise and echo, while f B Y b ′ E b ′ suppresses the echo more.
  • f A ( ⁇ ) suppresses only noise
  • f B ( ⁇ ) suppresses the echo.
  • this noise and echo suppression gain is combined with the spatial feature probability indicator or gain for form a raw combined gain.
  • the raw combined gain is post-processed by a post-processor 1025 and by post processing step 225 to ensure stability and other desired behavior.
  • the gain function f B Y b ′ E b ′ specific to the echo suppression is applied as a gain (after post-processing by post-processor 1025 and by post processing step 225 in embodiments that include postprocessing).
  • Post-processing is described in more detail herein below.
  • Some embodiments of gain calculator 129 includes a determined of the additional echo suppression gain and a combiner 1027 of the additional echo suppression gain with the post-processed gain to result in the overall B gains to apply. The inventors discovered that such an embodiment can provide a more specific and deeper attenuation of echo.
  • the echo probability indicator or gain f B Y b ′ E b ′ is not subject to the smoothing and continuity imposed by the post-processing 225, such post-processing, e.g., being tailored for the desired signal and noise signal stability, and a suitable level of noise suppression without unwanted voice distortion.
  • the need to eliminate echo from the signal can override the constraint of instantaneous speech quality when echo is active.
  • the echo suppressive component (after post-processing in embodiments that include post-processing) can apply narrow and potentially deep suppressive action across frequency, which can leave an unpleasant residual signature of the echo on the remaining noise in the signal.
  • a solution to this problem is that of "comfort noise" and it should be well known to some-one skilled in the art, and apparent how this could be applied to reduce the presence of gaps in the spectrum caused by an echo suppressor after the gain post processing.
  • Some embodiments of the gain calculator 129 include a post-processor 1025 and some embodiments of method 200 include a post-processing step 225.
  • Each of the post processor and post-processing step 225 is to post process the combined raw gains of the bands to generate a post-processed gain for each band.
  • Such post-processing includes in different embodiments one or more of: ensuring minimum gain values; ensuring there are no or few isolated or outlier gains by carrying out median filtering of the combined gain; and ensuring smoothness by carrying out one or both of time smoothing and band-to-band smoothing.
  • Some embodiments include signal classification, e.g., using one or both: a spatially-selective voice activity detector 1021 implementing a step 1111, and a wind activity detector 1023 implementing a step 1113 to generate a signal classification, such that the post-processing 225 of post-processor 1025 is according to the signal classification.
  • a spatially-selective voice activity detector 1021 is described herein below, as is an embodiment of a wind activity detector (WAD) 1023.
  • the signal classification controlled post-processing aspect of the invention is not limited to the particular embodiments of a voice activity detector or of a wind activity detector described herein.
  • the raw combined gain Gain' b,RAW may sometimes fall below a desired minimum point, that is, achieve more than a maximum desired suppression depth.
  • maximum suppression depth and minimum gain shall be uses interchangeably herein.
  • Not all the above-described embodiments for determining the gain include ensuring that the gain does not fall below such a minimum point.
  • the step of ensuring a minimum gain serves to stabilize the suppressive gain in noisy conditions by avoiding low gain values that can exhibit large relative variation with small errors in feature estimation or natural noise feature variations.
  • the process of setting a minimum gain serves to reduce processing artifacts and "musical noise" caused by such variation in the low valued gains, and also can be used to lessen the workload or depth of the suppression in certain bands which can lead to improved quality of the desired signal
  • post-processor 1025 and post processing step 225 include, e.g., in step 1115, ensuring that the gain does not fall below a pre-defined minimum, so that there is a pre-defined maximum suppression depth.
  • Gai n b , RAW ′ Gai n b , MIN ′ + 1 ⁇ Gai n b , MIN ′ ⁇ Gai n b , S ′ ⁇ Gai n b , N + E ′ .
  • the range of the maximum suppression depth or minimum gain may range from -80dB to -5dB and be frequency dependent.
  • the suppression depth was around -20dB at low frequencies below 200Hz, varying to be around -10dB at 1kHz and relaxing to be only - 6dB at the upper voice frequencies around 4kHz.
  • the processing of post-processing step 225 and of post-processor 1025 is controlled by a classification of the input signals, e.g., as being voice or not as determined by a VAD, and/or as being wind or not as determined by a WAD.
  • the minimum values of the gain for each band, Gain' b,MIN are dependent on a classification of the signal, e.g., whether the signal is determined to be voice by a VAD in embodiments that include a VAD, or to be wind by embodiments that include a WAD.
  • the VAD is spatially selective.
  • Gain' b,MIN is increased, e.g., in a frequency-band dependent way (or in another embodiment, by the same amount for each band b). In one embodiment, the amount of increase in the minimum is larger in the mid-frequency bands, e.g., bands between 500 Hz to 2kHz.
  • Gain' b,MIN is decreased, e.g., in a frequency-band dependent way (or in another embodiment, by the same amount for each band b). In one embodiment, the amount of decrease in the minimum is frequency dependent with a larger decrease occurring at the lower frequencies from 200Hz to 1500Hz.
  • the increase in minimum gain values is controlled to increase in a gradual manner over time as voice is detected, and similarly, to decrease in a gradual manner over time as lack of voice is detected after voice has been detected.
  • a single time constant is used to control the increase or decrease (for voice) and the decrease or increase (for wind).
  • a first time constant is used to control the increase in minimum gain values as voice is detected or the decrease as wind is detected
  • a second time constant is used to control the decrease in minimum gain values as lack of voice is detected after voice was detected, or the increase in minimum gain values as lack of wind is detected after wind was detected.
  • Such statistical outliers might occur in other types of processing in which an input signal is transformed and banded.
  • Such other types of processing include perceptual domain-based leveling, perceptual domain-based dynamic range control, and perceptual domain-based dynamic equalization that takes into account the variation in the perception of audio depending on the reproduction level of the audio signal. See, for example, International Application PCT/US2004/016964 , published as WO 2004111994 .
  • Perceptual-domain-based leveling, perceptual-domain-based dynamic range control, and perceptual-domain-based dynamic equalization processing each includes determining and adjusting the perceived loudness of an audio signal by applying a set of banded gains to a transformed and perceptually-banded metric of the amplitude of an input signal.
  • a psychoacoustic model is used to calculate a measure of the loudness of an audio signal in perceptual units.
  • perceptual domain loudness measure is referred to as specific loudness, and is a measure of perceptual loudness as a function of frequency and time.
  • true dynamic equalization is carried out in a perceptual domain to transform the perceived spectrum of the audio signal from a time-varying perceived spectrum to a substantially time-invariant perceived spectrum.
  • the gains determined for each band for leveling and/or dynamic equalization include statistical outliers, e.g., isolated values, and such outliers might cause artifacts such as musical noise.
  • the processing described herein may be applicable also to such other applications in which gains are applied to a signal indicative of transformed banded norms of the amplitude at a plurality of frequency bands.
  • the proposed post processing is also directly applicable to systems without the combination of features and suppression. For example, it provides an effective method for improving the performance of a single channel noise reduction system.
  • One embodiment of post-processing 225 and of post-processor 1025 includes, e.g. in step 1117, median filtering the raw gain over different frequency bands.
  • the median filter is characterized by 1) the number of gains to include to determine the median, and 2) the conditions used to extend the banded gains to allow calculation of the median at the edges of the spectrum.
  • One embodiment includes 3-point band-to-band median filtering, with extrapolation of interior values for the edges.
  • the minimum gain or a zero value is used to extend the banded gains.
  • the band-to-band median filtering is controlled by the signal classification.
  • a VAD e.g., a spatially-selective VAD is included, and if the VAD determines there is no voice, 5-point band-to-band median filtering is carried out, with extending the minimum gain or a zero value at the edges to compute the median, and if the VAD determines there is voice present, 3-point band-to-band median filtering is carried out, extrapolating the edge values at the edges to calculate the median.
  • post-processor 1025 and post-processing step 225 include smoothing 1119 across the bands to eliminate such potential jumps which can cause colored and unnatural output spectra.
  • smoothing 1119 uses a weighted moving average with a fixed kernel.
  • One example uses a binomial approximation of a Gaussian weighting kernel for the weighted moving average.
  • a 3-point binomial smoother has a kernel 1 4 1 2 1 .
  • weighted moving average filters are known, and any such filter can suitably be modified to be used for the band-to-band smoothing of the gain.
  • the application of the gains on the N frequency bins in step 227 and in element 131 includes using an N by B matrix.
  • the B by B matrix that defined smoothing can be combined with the gain application matrix to define a combined N by B matrix.
  • each of the gain applications of element 131 and the step 227 incorporates band-to-band smoothing.
  • the band-to-band median filtering is controlled by the signal classification.
  • a VAD e.g., a spatially-selective VAD is included, and if the VAD determines there is voice, the degree of smoothing is increased when noise is detected.
  • 5-point band-to-band weighted average smoothing is carried out in the case the VAD indicates noise is detected, else, when the VAD determines there is no voice, no smoothing is carried out.
  • time smoothing of the gains also is included.
  • Gain b is the current time-frame gain
  • Gain b,Smoothed is the time-smoothed gain
  • Gain b,Smoothed Pr ev is Gain b,Smoothed from the previous M -sample frame.
  • ⁇ b is a time constant which may be frequency band dependent and is typically in the range of 20 to 500ms. In one embodiment a value of 50ms was used.
  • first order time smoothing of the gains according to a set of first order time constants is included.
  • the amount of time smoothing is controlled by the signal classification of the current frame.
  • the signal classification of the current frame is used to control the values set of first order time constants used to filter the gains over time in each band.
  • one embodiment stops time smoothing in the case voice is detected.
  • the parameters of post-processing are controlled by the immediate signal classifier (VAD, WAD) value that has low latency and is able to achieve a rapid transition of the post-processing from noise into voice (or other desired signal) mode.
  • VAD immediate signal classifier
  • WAD voice-based signal classifier
  • VADs are known in the art.
  • optimal VADs are known, and there has been much research on how to determine such an "optimal VAD” according to a VAD optimality criterion.
  • one aspect of the invention is the inclusion of a plurality of VADs, each controlled by a small set of tuning parameters that separately control sensitivity and selectivity, including spatial selectivity, such parameters tuned according to the suppression elements the VAD is used in.
  • Each of the plurality of the VADs is an instantiation of a universal VAD that determines indications of voice activity from Y' b .
  • the universal VAD is controlled by a set of parameters and uses an estimate of noise spectral content, the banded frequency domain amplitude metric representation of the echo, and the banded spatial features.
  • the set of parameters includes whether the estimate of noise spectral content is spatially selective or not.
  • the type of indication of voice activity an instantiation determines controlled by a selection of the parameters.
  • another feature of embodiments of the invention is a method of determining a plurality of indications of voice activity from Y b ' , the mixed-down banded instantaneous frequency domain amplitude metric, the indications using respective instantiations of a universal voice activity detection method.
  • the universal voice activity detection method is controlled by a set of parameters and uses an estimate of noise spectral content, the banded frequency domain amplitude metric representation of the echo, and the banded spatial features.
  • the set of parameters including whether the estimate of noise spectral content is spatially selective or not. Which indication of voice activity an instantiation determines controller by a selection of the parameters.
  • selectivity is important, that is, the VAD instantiation should have a high probability that what it is detecting is voice
  • sensitivity is important, that is, the VAD instantiation should have a low probability of missing voice activity, even at the cost of selectivity so that more false positives are tolerated.
  • the VAD 125 used to prevent updating of the echo prediction parameters-the prediction filter coefficients- is selected to have a high sensitivity, even at the cost of selectivity.
  • the inventors selected to tune a VAD to have a balance of selectivity and sensitivity as being overly sensitive would lead to fluctuation of levels in noise as speech was falsely detected, whilst being overly selective would lead to some loss of voice.
  • the measurement of output speech level requires a VAD that is highly selective, but not overly sensitive to ensure that only actual speech is used to set the level and gain control.
  • a binary decision or classifier can be obtained by considering the test S > S thresh as indicating the presence of voice. It should also be apparent that the value S can be used as a continuous indicator of the instantaneous speech level.
  • an improved useful universal VAD for operations such as transmission control or controlling the post processing could be obtained using a suitable "hang over" or period of continued indication of voice after a detected event. Such a hang over period may vary from 0 to 500ms, and in one embodiment a value of 200ms was used. During the hang over period, it can be useful to reduce the activation threshold, for example by a factor of 2/3. This creates increased sensitivity to voice and stability once a talk burst has commenced.
  • the noise in the above expression is N b , S ' determined using the out-of-beam power (or other frequency domain amplitude metric) Y b ' .
  • the values of ⁇ N , ⁇ E are not necessarily the same as for the echo update VAD 125.
  • This VAD is called a spatially-selective VAD and is shown as element 1021 in FIG. 10 .
  • Y sens is set to be around expected microphone and system noise level, obtained by experiments on typical components.
  • ⁇ N , ⁇ E ,Y sens ,S thresh , BeamGainExp, and whether N b ' or N b , S ' is used are tunable parameters, each tuned according to the function performed by the element in which an instantiation of the universal VAD is used. This is to enhance the voice quality while improving the suppression of undesired effects such as one or more of echoes, noise, and sounds from other than the speaker location.
  • Other uses for the VAD structures presented herein include the control of transmission or coding, level estimation, gain control and system power management.
  • Some embodiments of the invention include a wind activity detector 1023 and wind activity detection step 1113 in the application of the gains, and in particular, in the post-processing.
  • each of wind activity detector (WAD) 1023 and wind detecting step 1113 operates to detect the presence of corrupting wind influences in the plurality of inputs, e.g., microphone inputs, e.g., two microphone inputs.
  • the element 1023 and step 1113 determine an estimate of wind activity. This can be used to control post-processing of the gains, e.g., to control one or more characteristics of one or more of: (a) imposing minimum gain values; (b) applying a median filter to gains across frequency bands; (c) band-to-band smoothing, (d) time smoothing, and other post-processing methods that in one embodiment are gated by voice activity, and in another by one or more of voice activity detection, wind activity detection, and silence detection.
  • a wind activity detector 1023 and a wind activity detection method 1113 use the following determined features for wind detection:
  • RatioStd is the standard deviation of the Ratio expressed in dB (10log 10 ( R b 22 / R b 11 )) across this set of bands.
  • CoherenceStd is the standard deviation of Coherence expressed in dB 5 log 10 R b 12 R b 21 R b 11 R b 22 across the set of bands, while in another, a non-logarithmic scale is used.
  • Slope is the spectral slope, obtained from the current frame of data
  • WindSlopeBias and WindSlope are constants empirically determined, e.g., from plots of the power, in one embodiment arriving at the values -5 and -20, to achieve a scaling of the SlopeContribution such that 0 corresponds to no wind, 1 represents a nominal wind, and values greater 1 indicating progressively higher wind activity.
  • CoherStd is obtained from the current frame of data and WindCoherStd is a constant empirically determined from Coherence data over time to achieve a scaling of CoherContribution with the values 0 and 1 representing the absence and nominal level of wind as above.
  • the overall wind level is then computed as the product Slope Contribution, RatioContribution, and CoherContribution and clamped to a sensible pre-defined level, for example 2.
  • This overall wind level is a continuous variable with a value of 1 representing a reasonable sensitivity to wind activity.
  • This sensitivity can be increased or decreased as required for different detection requirements to balance sensitivity and specificity as needed.
  • the signal can be further processed with smoothing or scaling to achieve the indicator of wind required for different functions.
  • a 100ms decay filter is used.
  • WindLevel S l o p e C o n t r i b u t i o n AND R a t i o C o n t r i b u t i o n I n d AND CoherContributionInd
  • SlopeContributionInd, RatioContributionInd, and CoherContributionInd are the wind activity indicators based on SlopeContribution, RatioContribution, and CoherContribution, respectively.
  • the presence of wind is confirmed only if all three features indicate some level of wind activity.
  • Such an implementation achieves a desired reduction in "false alarms", since for example whilst the Slope feature may register wind activity during some speech activity, the Ratio and Coherence features do not.
  • a filter may be used to filter the WindLevel signal issuing from the wind detector. Due to the nature of wind and aspects of the detection method, this value can vary rapidly.
  • the filter is provided to create a signal more suitable for the control of the post-processing (and for suppressing wind) by providing a certain robustness by adding some hysteresis that captures the rapid onset of wind, but maintains a memory of wind activity for a small time after the initial detection. In one embodiment this is achieved with a filter having low attack time constant, so that peaks in the detected level are quickly passed through, and a release time constant of the order of 100ms.
  • a suitable threshold for creating a binary indicator of wind activity would sensibly be in the range of 0.2 to 1.5.
  • a value of 1.0 was used against FilteredWindLevel to create a single binary indicator of wind.
  • G' b , b 1, ..., B, the B overall gains obtained after processing, and in those embodiments that include independent (additional) application of echo suppression, combining with the additional echo suppression gain.
  • the interpolation window is a raised cosine.
  • another widow such as a shape preserving spline, or other band-limited interpolation function is used.
  • the output syntheses process of step 229 is, in the case that the output is in the form of time samples, a conventional overlap add and inverse transform step, carried out, e.g., by output synthesizer/transformer 133.
  • the output remapping process of step 229 is, in the case that the output is in the frequency domain, a remapper as needed for the following step, and carried out, e.g., by output remapper 133.
  • a remapper as needed for the following step, and carried out, e.g., by output remapper 133.
  • only time domain samples are output, in others only remapped frequency domain output is generated, while in yet other embodiments, both time domain output and remapped frequency domain output is generated. See FIGS. 3D and 3E .
  • a processing apparatus including a processing system
  • Such an apparatus may be included, for example, in a headphone set such as a Bluetooth headset.
  • the audio inputs 101, the reference input(s) 102 and the audio output 135 are assumed to be in the form of frames of M samples of sampled data.
  • a digitizer including an analog-to-digital converter and quantizer would be present.
  • a de-quantizer and a digital-to-analog converter would be present.
  • FIG. 16 includes a processing system 1603 that is configured in operation to carry out the suppression methods described herein.
  • the processing system 1603 includes at least one processor 1605, which can be the processing unit(s) of a digital signal processing device, or a CPU of a more general purpose processing device.
  • the processing system 1603 also includes a storage subsystem 1607 typically including one or more memory elements.
  • the elements of the processing system are coupled, e.g., by a bus subsystem or some other interconnection mechanism not shown in FIG. 16 .
  • Some of the elements of processing system 1603 may be integrated into a single circuit, using techniques commonly known to one skilled in the art.
  • the storage subsystem 1607 includes instructions 1611 that when executed by the processor(s) 1605, cause carrying out of the methods described herein.
  • the storage subsystem 1607 is configured to store one or more tuning parameters 1613 that can be used to vary some of the processing steps carried out by the processing system 1603.
  • the system shown in FIG. 16 can be incorporated in a specialized device such as a headset, e.g., a wireless Bluetooth headset.
  • a headset e.g., a wireless Bluetooth headset.
  • the system also can be part of a general purpose computer, e.g., a personal computer configured to process audio signals.
  • processor may refer to any device or portion of a device that processes electronic data, e.g., from registers and/or memory to transform that electronic data into other electronic data that, e.g., may be stored in registers and/or memory.
  • a "computer” or a “computing machine” or a “computing platform” may include one or more processors.
  • the methodologies described herein are, in some embodiments, performable by one or more processors that accept logic, e.g., instructions encoded on one or more computer-readable media. When executed by one or more of the processors, the instructions cause carrying out at least one of the methods described herein. Any processor capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken is included. Thus, one example is a typical processing system that includes one or more processors. Each processor may include one or more of a CPU or similar element, a graphics processing unit (GPU), field-programmable gate array, application-specific integrated circuit, and/or a programmable DSP unit.
  • GPU graphics processing unit
  • DSP programmable DSP unit
  • the processing system further includes a storage subsystem with at least one storage medium, which may include memory embedded in a semiconductor device, or a separate memory subsystem including main RAM and/or a static RAM, and/or ROM, and also cache memory.
  • the storage subsystem may further include one or more other storage devices, such as magnetic and/or optical and/or further solid state storage devices.
  • a bus subsystem may be included for communicating between the components.
  • the processing system further may be a distributed processing system with processors coupled by a network, e.g., via network interface devices or wireless network interface devices.
  • the processing system requires a display, such a display may be included, e.g., a liquid crystal display (LCD), organic light emitting display (OLED), or a cathode ray tube (CRT) display.
  • a display e.g., a liquid crystal display (LCD), organic light emitting display (OLED), or a cathode ray tube (CRT) display.
  • the processing system also includes an input device such as one or more of an alphanumeric input unit such as a keyboard, a pointing control device such as a mouse, and so forth.
  • the term storage device, storage subsystem, or memory unit as used herein, if clear from the context and unless explicitly stated otherwise, also encompasses a storage system such as a disk drive unit.
  • the processing system in some configurations may include a sound output device, and a network interface device.
  • a non-transitory computer-readable medium is configured with, e.g., encoded with instructions, e.g., logic that when executed by one or more processors of a processing system such as a digital signal processing device or subsystem that includes at least one processor element and a storage subsystem, cause carrying out a method as described herein. Some embodiments are in the form of the logic itself.
  • a non-transitory computer-readable medium is any computer-readable medium that is statutory subject matter under the patent laws applicable to this disclosure, including Section 101 of Title 35 of the United States Code.
  • a non-transitory computer-readable medium is for example any computer-readable medium that is not specifically a transitory propagated signal or a transitory carrier wave or some other transitory transmission medium.
  • non-transitory computer-readable medium thus covers any tangible computer-readable storage medium.
  • the storage subsystem thus includes a computer-readable storage medium that is configured with, e.g., encoded with instructions, e.g., logic, e.g., software that when executed by one or more processors, causes carrying out one or more of the method steps described herein.
  • the software may reside in the hard disk, or may also reside, completely or at least partially, within the memory, e.g., RAM and/or within the processor registers during execution thereof by the computer system.
  • the memory and the processor registers also constitute a non-transitory computer-readable medium on which can be encoded instructions to cause, when executed, carrying out method steps.
  • Non-transitory computer-readable media include any tangible computer-readable storage media and may take many forms including non-volatile storage media and volatile storage media.
  • Non-volatile storage media include, for example, static RAM, optical disks, magnetic disks, and magneto-optical disks.
  • Volatile storage media includes dynamic memory, such as main memory in a processing system, and hardware registers in a processing system.
  • While the computer-readable medium is shown in an example embodiment to be a single medium, the term "medium” should be taken to include a single medium or multiple media (e.g., several memories, a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions.
  • a non-transitory computer-readable medium e.g., a computer-readable storage medium may form a computer program product, or be included in a computer program product.
  • the one or more processors operate as a standalone device or may be connected, e.g., networked to other processor(s), in a networked deployment, or the one or more processors may operate in the capacity of a server or a client machine in server-client network environment, or as a peer machine in a peer-to-peer or distributed network environment.
  • the term processing system encompasses all such possibilities, unless explicitly excluded herein.
  • the one or more processors may form a personal computer (PC), a media playback device, a headset device, a hands-free communication device, a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a game machine, a cellular telephone, a Web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • PC personal computer
  • PDA Personal Digital Assistant
  • game machine a cellular telephone
  • Web appliance a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine.
  • embodiments of the present invention may be embodied as a method, an apparatus such as a special purpose apparatus, an apparatus such as a data processing system, logic, e.g., embodied in a non-transitory computer-readable medium, or a computer-readable medium that is encoded with instructions, e.g., a computer-readable storage medium configured as a computer program product.
  • the computer-readable medium is configured with a set of instructions that when executed by one or more processors cause carrying out method steps.
  • aspects of the present invention may take the form of a method, an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
  • the present invention may take the form of program logic, e.g., a computer program on a computer-readable storage medium, or the computer-readable storage medium configured with computer-readable program code, e.g., a computer program product.
  • embodiments of the present invention are not limited to any particular implementation or programming technique and that the invention may be implemented using any appropriate techniques for implementing the functionality described herein. Furthermore, embodiments are not limited to any particular programming language or operating system.
  • an element described herein of an apparatus embodiment is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.
  • the invention is not limited to use of power, i.e., the weighted sum of the squares of the frequency coefficient amplitudes, and can be modified to accommodate any metric of the amplitude.
  • any one of the terms comprising, comprised of or which comprises is an open term that means including at least the elements/features that follow, but not excluding others.
  • the term comprising, when used in the claims should not be interpreted as being limitative to the means or elements or steps listed thereafter.
  • the scope of the expression a device comprising element_A and element_B should not be limited to devices consisting of only elements element_A and element_B.
  • Any one of the terms including or which includes or that includes as used herein is also an open term that also means including at least the elements/features that follow the term, but not excluding others. Thus, including is synonymous with and means comprising.

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  • Engineering & Computer Science (AREA)
  • Human Computer Interaction (AREA)
  • Quality & Reliability (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Circuit For Audible Band Transducer (AREA)
  • Cable Transmission Systems, Equalization Of Radio And Reduction Of Echo (AREA)
  • Soundproofing, Sound Blocking, And Sound Damping (AREA)

Claims (15)

  1. Système (100) pour traiter des signaux d'entrée audio (101), comprenant :
    un processeur d'entrée (103, 107, 109) pour accepter une pluralité de signaux d'entrée audio échantillonnés pour former un signal abaissé en fréquence (108) dans l'échantillon ou le domaine fréquentiel et, en outre, pour former une métrique d'amplitude de domaine fréquentiel instantanée en bandes abaissée en fréquence (110) des signaux d'entrée (101) pour une pluralité de bandes de fréquences, au moins 90 % des bandes ayant une contribution de deux, ou davantage, groupes de fréquences ;
    un estimateur de caractéristiques spatiales en bandes (105) pour estimer des caractéristiques spatiales en bandes (106) à partir de la pluralité de signaux d'entrée échantillonnés ;
    un calculateur de gain (129) pour calculer un ensemble d'indicateurs de probabilité de suppression en bandes comprenant un indicateur de probabilité de signal hors site en bandes (1012) déterminé en utilisant deux, ou davantage, des caractéristiques spatiales en bandes (106), et un indicateur de probabilité de suppression de bruit en bandes (1014) indiquant pour chaque bande de fréquences un gain de suppression de bruit qui est déterminé en utilisant une estimation en bandes du contenu spectral de bruit sur la base de la métrique d'amplitude de domaine fréquentiel instantanée en bandes abaissée en fréquence des signaux d'entrée (101), le calculateur de gain étant en outre configuré pour combiner l'ensemble des indicateurs de probabilité pour calculer un gain combiné pour chaque bande de la pluralité des bandes de fréquences ; et
    un suppresseur (131) pour appliquer un gain final interpolé déterminé à partir des gains combinés (130) de la pluralité de bandes de fréquences pour procéder à une suppression sur le signal abaissé en fréquence pour former des données de signal supprimé (132).
  2. Système (100) tel que décrit dans la revendication 1, dans lequel les caractéristiques spatiales (106) sont déterminées à partir d'une ou plusieurs matrices de covariance pondérées en bandes des signaux d'entrée échantillonnés.
  3. Système tel que décrit dans l'une quelconque des revendications 1 à 2, comprenant en outre :
    un processeur d'entrée de signal de référence (111) pour accepter un ou plusieurs signaux de référence et pour former une représentation métrique d'amplitude de domaine fréquentiel en bandes (116) des un ou plusieurs signaux de référence ;
    un prédicteur (117) d'une représentation métrique d'amplitude de domaine fréquentiel en bandes (118) d'un écho, le prédicteur utilisant des coefficients déterminés de manière adaptative,
    où le gain final comprend au moins un indicateur de probabilité de suppression en bandes qui inclut la suppression d'écho, l'au moins un indicateur de probabilité de suppression en bandes étant déterminé au moyen d'une estimation spectrale d'écho en bandes déterminée à partir de la sortie du prédicteur (117).
  4. Système tel que décrit dans la revendication 3, comprenant en outre un dispositif de mise à jour de coefficient pour :
    mettre à jour (127) les coefficients (128) déterminés de manière adaptative en utilisant une estimation de la métrique d'amplitude de domaine fréquentiel spectral en bandes du bruit (124), un contenu spectral d'écho prédit précédemment (118), et une estimation de la métrique d'amplitude spectrale en bandes du signal abaissé en fréquence (110 ou 122).
  5. Système tel que décrit dans la revendication 4, comprenant en outre :
    un détecteur d'activité vocale avec une sortie couplée au dispositif de mise à jour de coefficient, le détecteur d'activité vocale utilisant l'estimation de la métrique d'amplitude spectrale en bandes du signal abaissé en fréquence (110 ou 122), l'estimation de la métrique d'amplitude spectrale en bandes du bruit (124), et le contenu spectral d'écho prédit précédemment (118),
    où la mise à jour par le dispositif de mise à jour de coefficient dépend de la sortie du détecteur d'activité vocale.
  6. Système tel que décrit dans l'une quelconque des revendications 3 à 5, dans lequel l'estimation de la métrique d'amplitude de domaine fréquentiel spectral en bandes du bruit utilisée par le dispositif de mise à jour de coefficient est déterminée par un suiveur de minimum à fuite avec un taux de poursuite défini par au moins un paramètre de taux de fuite de suiveur de minimum.
  7. Système tel que décrit dans l'une quelconque des revendications 1 à 6, dans lequel le processeur d'entrée (103, 107, 109) comprend des transformateurs d'entrée (103) pour effectuer des transformations en groupes de fréquences, un mélangeur abaisseur (107) pour former le signal abaissé en fréquence (108) dans l'échantillon ou le domaine de groupe de fréquences, et un élément de mise en bandes spectrales (109) pour former la métrique d'amplitude de domaine fréquentiel instantanée en bandes abaissée en fréquence (101) pour les bandes de fréquences.
  8. Système (100) tel que décrit dans l'une quelconque des revendications 1 à 7, dans lequel le calculateur de gain est en outre adapté pour post-traiter le gain combiné des bandes pour générer un gain post-traité (130) pour chaque bande, de manière à ce que le gain final interpolé soit déterminé à partir des gains post-traités des bandes.
  9. Système tel que décrit dans l'une quelconque des revendications 3 à 8, dans lequel les coefficients déterminés de manière adaptative sont déterminés en utilisant un signal d'activité vocale déterminé par un détecteur d'activité vocale (125), une estimation de la métrique d'amplitude spectrale en bandes du bruit (124), une estimation de la métrique d'amplitude spectrale en bandes du signal abaissé en fréquence, et un contenu spectral d'écho prédit précédemment.
  10. Système tel que décrit dans l'une quelconque des revendications 1 à 9, dans lequel l'indicateur de probabilité de suppression de bruit pour chaque bande de fréquences indique une fonction de gain de suppression de bruit de la métrique d'amplitude instantanée en bandes pour la bande,
    où, pour chaque bande de fréquences, une plage de valeurs de valeurs métriques d'amplitude instantanée en bandes est attendue pour le bruit, et une seconde plage de valeurs de valeurs métriques d'amplitude instantanée en bandes est attendue pour une entrée souhaitée, et
    où les fonctions de gain de suppression de bruit pour les bandes de fréquences sont configurées pour :
    avoir une valeur minimale respective ;
    avoir une valeur relativement constante ou un gradient négatif relativement faible dans la plage ;
    avoir un gain relativement constant dans la seconde plage ; et
    présenter une transition en douceur de la plage vers la seconde plage.
  11. Procédé (200) pour faire fonctionner un appareil de traitement (100) pour supprimer des signaux indésirables comprenant du bruit et des signaux hors site dans des signaux d'entrée audio (101), le procédé comprenant les étapes suivantes :
    accepter (201), dans l'appareil de traitement, une pluralité de signaux d'entrée audio échantillonnés (101) ;
    former (203, 207, 209) une métrique d'amplitude de domaine fréquentiel instantanée en bandes abaissée en fréquence (110) des signaux d'entrée (101) pour une pluralité de bandes de fréquences, la formation comprenant d'effectuer une transformation (203) en valeurs de domaine fréquentiel à valeurs complexes des signaux d'entrée (101) ou d'un signal abaissé en fréquence pour un ensemble de groupes de fréquences ;
    au moins 90 % des bandes ayant une contribution de deux, ou davantage, groupes de fréquences ;
    déterminer (205) des caractéristiques spatiales en bandes (106) à partir de la pluralité de signaux d'entrée échantillonnés ;
    calculer (223) un ensemble d'indicateurs de probabilité de suppression en bandes, comprenant un indicateur de probabilité de suppression hors site en bandes (1012) déterminé en utilisant deux, ou davantage, des caractéristiques spatiales en bandes (106), et un indicateur de probabilité de suppression de bruit en bandes (1014) pouvant être exprimé pour chaque bande comme un gain de suppression de bruit et déterminé en utilisant une estimation en bandes de contenu spectral de bruit (1006) déterminée sur la base de la métrique d'amplitude de domaine fréquentiel instantanée en bandes abaissée en fréquence du signal abaissé en fréquence (108) ;
    combiner l'ensemble d'indicateurs de probabilité en bandes pour déterminer un gain combiné pour chaque bande de la pluralité de bandes de fréquences ;
    appliquer (227) un gain final interpolé déterminé à partir des gains combinés de la pluralité de bandes de fréquences pour procéder à une suppression sur le signal abaissé en fréquence pour former des données de signal supprimé (132).
  12. Procédé (200) tel que décrit dans la revendication 11, dans lequel les caractéristiques spatiales (106) sont déterminées à partir d'une ou plusieurs matrices de covariance pondérées en bandes des signaux d'entrée échantillonnés.
  13. Procédé tel que décrit dans la revendication 11 ou la revendication 12, dans lequel la formation (215, 217) de la métrique d'amplitude de domaine fréquentiel en bandes abaissée en fréquence instantanée comprend de transformer (103) les entrées acceptées ou une combinaison de celles-ci en groupes de fréquences, d'effectuer un abaissement en fréquence dans l'échantillon ou le domaine du groupe de fréquences pour former un signal abaissé en fréquence, et de procéder à une mise en bandes spectrales pour former des bandes de fréquences.
  14. Procédé tel que décrit dans l'une quelconque des revendications 11 à 13, dans lequel l'indicateur de probabilité de suppression de bruit pour chaque bande de fréquences peut être exprimé comme une fonction de gain de suppression de bruit de la métrique d'amplitude instantanée en bandes pour la bande,
    où, pour chaque bande de fréquences, une plage de valeurs de valeurs métriques d'amplitude instantanée en bandes est attendue pour le bruit, et une seconde plage de valeurs de valeurs métriques d'amplitude instantanée en bandes est attendue pour une entrée souhaitée, et
    où les fonctions de gain de suppression de bruit pour les bandes de fréquences sont configurées pour :
    avoir une valeur minimale respective ;
    avoir une valeur relativement constante ou un gradient négatif relativement faible dans la plage ;
    avoir un gain relativement constant dans la seconde plage ; et
    présenter une transition en douceur de la plage vers la seconde plage.
  15. Support non transitoire lisible par ordinateur configuré avec des instructions qui, lorsqu'elles sont exécutées par au moins un processeur d'un système de traitement, amènent le matériel de traitement à exécuter un procédé tel que décrit dans l'une quelconque des revendications de procédé précédentes.
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WO2012109384A1 (fr) 2012-08-16
JP2014510452A (ja) 2014-04-24
EP2673778A1 (fr) 2013-12-18
CN103354937A (zh) 2013-10-16
EP2673777A1 (fr) 2013-12-18
CN103348408B (zh) 2015-11-25
WO2012109385A1 (fr) 2012-08-16
EP2673778B1 (fr) 2018-10-10
CN103348408A (zh) 2013-10-09
JP6002690B2 (ja) 2016-10-05
CN103354937B (zh) 2015-07-29

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