CN107393553B - Auditory feature extraction method for voice activity detection - Google Patents

Auditory feature extraction method for voice activity detection Download PDF

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CN107393553B
CN107393553B CN201710578645.7A CN201710578645A CN107393553B CN 107393553 B CN107393553 B CN 107393553B CN 201710578645 A CN201710578645 A CN 201710578645A CN 107393553 B CN107393553 B CN 107393553B
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CN107393553A (en
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蔡钢林
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Shenzhen Yajin Smart Technology Co ltd
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    • G—PHYSICS
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    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
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    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
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    • 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
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    • 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
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    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
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    • 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
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Abstract

The invention provides an auditory feature extraction method for voice activity detection, which comprises the following steps: calculating a time domain signal of the sound signal; calculating a prior signal-to-noise ratio gamma (k) and a posterior signal-to-noise ratio (k) of the sound signal by using the time domain signal, wherein k is a frequency coordinate; calculating auditory characteristics of the current frame according to the time domain signal, the prior signal-to-noise ratio gamma (k) and the posterior signal-to-noise ratio (k), wherein the auditory characteristics comprise a first dimension parameter, a second dimension parameter and a third dimension parameter; the first dimension parameter is related to the a priori signal-to-noise ratio γ (k), the second dimension parameter is related to the a posteriori signal-to-noise ratio (k), and the third dimension parameter is related to the time domain signal. The invention adopts the prior signal-to-noise ratio and the posterior signal-to-noise ratio to combine with the time domain signal to represent the auditory characteristics, and the extracted auditory characteristics can be used for comparing with the auditory threshold value to detect the real-time voice activity.

Description

Auditory feature extraction method for voice activity detection
Technical Field
The invention relates to the field of voice recognition, in particular to an auditory feature extraction method for voice activity detection.
Background
With the rapid development of internet technology and intelligent hardware in recent years, voice intelligent interaction technologies such as voice recognition, voiceprint recognition and sound source detection are beginning to move from laboratories to users. The voice recognition technology is the most core technology of a voice-based man-machine interaction system. The recognition rate has reached the available accuracy under defined conditions. By limited adjustment is generally meant that the user is closer to the microphone and less noisy. The requirement that the voice command must be issued in close proximity limits the ease of voice interaction.
In the case of far speech, the recognition rate is rapidly reduced because the speech energy is rapidly attenuated while the noise interference energy is substantially unchanged. Another factor affecting the recognition accuracy is that reverberation of the voice command after reaching the walls of the room after multiple reflections also causes mismatching between the actual application and the voice recognition training data set, and affects the recognition rate.
There are two main sources of noise: (1) the microphone signal acquires the channel noise of the system, the channel noise is different due to the sensitivity of the microphone, and the higher the sensitivity of the microphone is, the higher the channel noise is generally; (2) non-negligible ambient noise interference, such as television, air conditioning noise, etc. Reverberation is more complex and more difficult to suppress than noise due to the more complex conditions of generation. Also, noise and reverberation generally coexist, making reverberation suppression more difficult.
CN103559893A discloses an underwater target gamma-chirp cepstrum coefficient auditory feature extraction method, which uses a gamma-chirp auditory filter bank to output and form cepstrum coefficients, gives out an auditory feature vector of an underwater target, and can improve robustness of underwater target signal feature extraction under the condition of complex marine environment interference, thereby improving accuracy of underwater target identification.
The problem of voice recognition under a far-speaking condition is solved, and the auditory characteristics under the far-speaking condition need to be accurately extracted. However, the method provided by CN103559893A, the extracted auditory features of which are limited to underwater environment, is not suitable for speech recognition in the case of far speech.
There is also a super directional Beamforming technology, which employs an annular or linear microphone array to directionally enhance the directional signal of the target sound source through a set of spatial filters. The super-directional Beamforming technology is used for improving the quality of sound signals in terms of sampling. However, by adopting the super directional Beamforming technology, the number of microphones is required to be large, the requirements on the consistency of the microphones and the accuracy of the geometric positions of the microphones are high, the difficulty and the cost of hardware implementation are increased, the integration in most middle and low-level products is difficult, and the application range is very limited.
Disclosure of Invention
The invention mainly aims to provide an auditory feature extraction method for voice activity detection, which can effectively extract auditory features under far speaking conditions in a single-microphone system and improve voice recognition rate.
The invention provides an auditory feature extraction method for voice activity detection, which comprises the following steps:
acquiring a time domain signal of a sound signal;
calculating a prior signal-to-noise ratio gamma (k) and a posterior signal-to-noise ratio (k) of the sound signal by using the time domain signal, wherein k is a frequency coordinate;
calculating auditory characteristics of the current frame according to the time domain signal, the prior signal-to-noise ratio gamma (k) and the posterior signal-to-noise ratio (k), wherein the auditory characteristics comprise a first dimension parameter, a second dimension parameter and a third dimension parameter; the first dimension parameter is related to the a priori signal-to-noise ratio γ (k), the second dimension parameter is related to the a posteriori signal-to-noise ratio (k), and the third dimension parameter is related to the time domain signal.
Preferably, the first dimension parameter is represented by V (1), which can be obtained by the following formula:
Figure BDA0001350986280000021
the second dimension parameter is represented by V (2), which can be obtained by the following formula:
Figure BDA0001350986280000022
the third dimension parameter is represented by V (3), which can be obtained by the following formula:
Figure BDA0001350986280000023
where K is the number of the whole frequency band, LWIs representative of window length, LTRepresenting the starting sample point, the function y is the time domain mixed speech data, and j is the time variable.
Preferably, the prior signal-to-noise ratio γ (k) can be obtained by the following formula:
Figure BDA0001350986280000031
where l is the time frame coordinate, Y (l, k) is the mixed speech spectrum, ΦV(k) Representing the power spectral density of the noise signal.
Preferably, the posterior signal-to-noise ratio (k) can be determined by the following formula:
Figure BDA0001350986280000032
wherein beta is a smoothing factor, beta is a value range of 0.6 to 0.9,
Figure BDA0001350986280000033
to estimate the speech spectrum, the Max function represents the maximum of two variables chosen.
Preferably, β is 0.75.
Preferably, the time domain signal is represented by y (t), which can be represented by the following formula:
Figure BDA0001350986280000034
wherein, x (t) is a voice signal with reverberation, v (t) is background noise, h (τ) is a reverberation impact response signal, and s (t- τ) is a voice signal without reverberation.
Preferably, before calculating the prior signal-to-noise ratio γ (k) and the posterior signal-to-noise ratio (k) of the sound signal by using the time domain signal, further comprises,
initializing voice parameters including noise power spectral density ΦV(k) Observing the power spectral density phi of the signalY(k) Estimating the speech spectrum
Figure BDA0001350986280000035
A priori signal-to-noise ratio γ (k) and a posteriori signal-to-noise ratio (k), the initialization procedure is as follows:
l before settingITime frameWith voice activity, then
Figure BDA0001350986280000036
Figure BDA0001350986280000037
Figure BDA0001350986280000038
γ(k)＝1，(k)＝κ，k＝1，2，...，K
Where K is the number of the whole band, 1 is the time frame coordinate, Y (l, K) is the mixed speech spectrum, κ is the attenuation factor, ΦV(k) Power spectral density, phi, representing noise signalY(k) Representing the power spectral density of the observed signal,
Figure BDA0001350986280000041
to estimate the speech spectrum.
Preferably, after the initializing the voice parameters, further comprising,
according to the power spectral density of the observation signal of the previous frame, obtaining an observation signal power spectral density estimated value of the next frame smoothly, wherein the observation signal power spectral density estimated value can be obtained by the following formula:
Φ′Y(k)＝αΦY(k)+(1-α)|Y(l，k)|2
wherein alpha is a smoothing factor and has a value range of 0.95-0.995.
Preferably, after obtaining the power spectral density estimation value of the observed signal of the next frame according to the power spectral density of the observed signal of the previous frame by smoothing, the method further comprises,
calculating a noise power spectrum adaptive updating step length, wherein the noise power spectrum adaptive updating step length can be obtained by the following formula:
Figure BDA0001350986280000042
wherein the smoothing factor alpha is taken as a fixed step.
Preferably, after the step size is adaptively updated by calculating the noise power spectrum, further comprising,
updating the noise power spectrum according to the self-adaptive updating step length of the noise power spectrum, wherein the noise power spectrum can be obtained by the following formula:
ΦV(k)＝αV(k)Φ′V(k)+(1-αV(k))|Y(l，k)|2。
the invention provides an auditory feature extraction method for voice activity detection, which adopts a priori signal-to-noise ratio and a posteriori signal-to-noise ratio to combine with a time domain signal to represent auditory features, and the extracted auditory features can be used for comparing with an auditory threshold value to detect real-time voice activity.
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FIG. 1 is a flow chart illustrating an embodiment of an auditory feature extraction method for voice activity detection according to the present invention;
fig. 2 is a schematic diagram of a hanning window.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The sound signal referred to in the present invention is digital audio data, that is, digital audio data obtained by converting a sound wave into an analog audio signal by a sound wave conversion circuit and then converting the analog audio signal by an analog-to-digital converter.
Referring to fig. 1, the present invention provides an auditory feature extraction method for voice activity detection, comprising the following steps:
s10, acquiring a time domain signal of the sound signal;
s20, calculating the prior signal-to-noise ratio gamma (k) and the posterior signal-to-noise ratio (k) of the sound signal by using the time domain signal, wherein k is a frequency coordinate;
s30, calculating auditory characteristics of the current frame according to the time domain signal, the prior signal-to-noise ratio gamma (k) and the posterior signal-to-noise ratio (k), wherein the auditory characteristics comprise a first dimension parameter, a second dimension parameter and a third dimension parameter; the first dimension parameter is related to the a priori signal-to-noise ratio γ (k), the second dimension parameter is related to the a posteriori signal-to-noise ratio (k), and the third dimension parameter is related to the time domain signal.
In step S10, the sound signal refers to the mixed voice data acquired by the sound collection system, and is usually stored in a buffer. Assuming that the mixed speech data is y (t), it can be considered as a convolution of the reverberated speech signal x (t) and the background noise v (t). Mixing speech data into y (t) is one of time domain signals of a sound signal. The reverberated speech signal x (t) can in turn be considered as a convolution of the reverberant impulse response signal h (τ) and the non-reverberated speech s (t- τ). The mathematical formula can be expressed as:
Figure BDA0001350986280000051
the above is only one way of acquiring the time domain signal of the sound signal, and the time domain signal of the sound signal may be acquired in other forms.
In step S20, the prior snr can be obtained by the following formula:
Figure BDA0001350986280000052
where 1 is the time frame coordinate, Y (l, k) is the mixed speech spectrum, ΦV(k) Representing the power spectral density of the noise signal.
Y (l, k) is obtained by FFT transformation of the mixed speech data Y (t), and the specific transformation is as follows:
Figure BDA0001350986280000053
where w (t) is a Hanning window of length 512. The waveform of the hanning window is shown in fig. 2.
The posterior signal-to-noise ratio can be obtained by the following formula:
Figure BDA0001350986280000061
wherein beta is a smoothing factor, beta is a value range of 0.6 to 0.9,
Figure BDA0001350986280000062
to estimate the speech spectrum, the Max function represents the maximum of two variables chosen. In this embodiment, the value is preferably 0.75.
The above is only a preferred calculation method of the prior snr and the posterior snr, and any method of performing appropriate deformation decomposition and then performing solution according to the above method should also fall within the scope of the present invention.
In step S30, the auditory characteristics of the current frame include a first dimension parameter V (1), a second dimension parameter V (2), and a third dimension parameter V (3), and are stored in the buffer. Auditory features are computed in the form of three-dimensional column vectors. The auditory characteristics of the current frame may be represented in the following manner:
Figure BDA0001350986280000063
v (1) can be obtained by the following equation:
Figure BDA0001350986280000064
v (2) can be obtained by the following equation:
Figure BDA0001350986280000065
v (3) can be obtained by the following equation:
Figure BDA0001350986280000066
where K is the number of the whole frequency band, LWIs representative of window length, LTRepresenting the starting sample point, the function y is the time domain mixed speech data, and j is the time variable.
The above is only a preferred calculation method of the first dimension parameter V (1), the second dimension parameter V (2) and the third dimension parameter V (3), and any method of performing appropriate deformation decomposition and then performing solution according to the above method should also fall within the protection scope of the present invention.
The following is a specific calculation process of the auditory characteristics.
Firstly, the estimation of background noise, and the accuracy of the noise energy estimation directly influences the effect of subsequent voice detection. The embodiment of the invention adopts a mode of combining fixed noise estimation with noise self-adaptive updating to ensure the stability and accuracy of the noise estimation. The initialization and specific calculation flow is as follows:
(1) taking the data of the buffer area, windowing the data to perform FFT (fast Fourier transform), and transforming a time domain signal to a frequency spectrum domain:
assume that the mixed speech data is y (t), where x (t) is a speech signal with reverberation, v (t) is background noise, h (τ) is a reverberation impulse response signal, and s (t- τ) is a non-reverberation speech signal. The FFT (fourier transform) is as follows:
Figure BDA0001350986280000071
Figure BDA0001350986280000072
where w (t) is a Hanning window of length 512, l is a time frame coordinate, and k is a frequency coordinate.
(2) To front LIThe time frame assumes no voice activity and is initialized as follows:
Figure BDA0001350986280000073
Figure BDA0001350986280000074
Figure BDA0001350986280000075
γ(k)＝1，(k)＝κ，k＝1，2，...，K
where K represents the number of the whole frequency band, phiV(k) Power spectral density, phi, representing noise signalY(k) Representing the power spectral density of the observed signal, gamma (k) is the a priori signal-to-noise ratio, (k) is the a posteriori signal-to-noise ratio,
Figure BDA0001350986280000076
to estimate the speech spectrum, it is initialized to multiply the mean of the mixed spectrum by an attenuation factor k, which takes a value of 0.1.
(3) From LTAnd starting iterative calculation at +1 time frame, wherein the calculation flow is as follows:
(3.1) updating the power spectral density estimated value of the observation signal, namely, according to the result of the previous frame, smoothly obtaining the calculation result of the next frame:
Φ′Y(k)＝αΦY(k)+(1-α)|Y(l，k)|2
wherein α is a smoothing factor, a value range is recommended to be 0.95-0.995, and 0.98 is preferably used as a smoothing threshold in this embodiment.
(3.2) calculating the prior and posterior signal-to-noise ratios
Figure BDA0001350986280000077
Figure BDA0001350986280000081
Wherein β is a smoothing factor, β is a value range of 0.6 to 0.9, and a value of 0.75 is preferred in this embodiment. The Max function represents the selection of the maximum of the two variables.
(3.3) calculating the self-adaptive updating step length of the noise power spectrum according to the prior signal-to-noise ratio and the posterior signal-to-noise ratio:
Figure BDA0001350986280000082
namely, a mode of adding a fixed step length and a self-adaptive step length is adopted to realize the whole updating.
(3.4) updating the noise power spectrum according to the step length, wherein the basic principle is that if the voice is less, the step length of updating the noise power spectrum is larger, and the accuracy of noise estimation is ensured; otherwise, a slower step size is used to avoid the speech signal from participating in the iterative update of the noise power spectrum:
ΦV(k)＝αV(k)Φ′V(k)+(1-αV(k))|Y(l，k)|2。
the output of the above equation is the noise power spectrum update result, which is used for the noise update of the next frame and participating in the voice detection process as a parameter.
After the background noise parameters are accurately estimated, auditory features can be constructed based on the background noise parameters. After the auditory characteristics are obtained, the auditory characteristics of the current frame are compared with a set auditory threshold value, and whether the current frame has voice activity or not can be judged.
The voice activity detection is mainly used for detecting a voice activity area, stopping the optimization processing of voice in a non-voice activity area and reducing power consumption; in the voice activity area, noise interference can be reduced, and the voice optimization effect is improved.
Before extracting the auditory features of the current frame, there is an initialization process, which is as follows:
initializing a characteristic buffer matrix, a characteristic threshold value and a voice detection result buffer area, wherein the characteristic buffer area matrix is formed by LIThe 3-dimensional column vectors are formed and are formulated as follows:
Figure BDA0001350986280000083
Figure BDA0001350986280000084
Figure BDA0001350986280000085
Q(1：LI)＝0
θT(1)＝FB(1，1)
θT(2)＝FB(2，1)
θT(3)＝FB(3，1)
wherein, FBIs an auditory feature buffer, Q is a voice activity detection result buffer, θTThe threshold buffer for the auditory feature, i.e. the prior signal-to-noise ratio, the posterior signal-to-noise ratio and the time domain signal are used for the final voice activity detection, respectively. In the auditory feature calculation, LWIs representative of window length, LTThe value range of the start sample point is usually between 5 and 20, and is set to 10 in this embodiment.
From LTStarting with +1 time frame, the current frame auditory features are computed as follows:
Figure BDA0001350986280000091
Figure BDA0001350986280000092
Figure BDA0001350986280000093
after the current frame auditory feature calculation result is obtained, the feature buffer area and the feature threshold are updated, the current auditory feature is used for comparing with the auditory threshold, and the voice detection result is determined according to the comparison result. The method comprises the following specific steps:
according to the current frame auditory characteristic calculation result, updating the characteristic buffer area and the characteristic threshold value, namely kicking the data with the longest time in the buffer area out of the buffer area, and putting the current frame data into the buffer area:
Figure BDA0001350986280000094
and calculating the hearing threshold corresponding to each dimension parameter:
Figure BDA0001350986280000095
comparing the current auditory characteristics with an auditory threshold, determining a voice detection result according to the comparison result, and specifically calculating as follows:
Figure BDA0001350986280000096
Figure BDA0001350986280000097
q (i) is the score of the dimensional parameter of the auditory feature, QFrameAnd if the result is a judgment result of the voice check, the result is 1, the current frame has voice, and if the result is 0, the current frame has no voice.
Updating a voice detection result buffer area, kicking out the data with the longest time in the buffer area from the buffer area, adding a current frame judgment result, and calculating an average voice detection result in the buffer area:
Q＝[Q′(：，2：LB)；QFrame]
then, calculating the statistical value of the detection results in the voice detection result buffer, wherein the sum of the detection results is calculated as follows:
Figure BDA0001350986280000101
since speech is usually continuous, the contrast QMWith a fixed threshold value LIIf the value is smaller than the threshold value, the frame of the speech in the current buffer area is indicated to be false detection, no speech exists in the current buffer area, the characteristic threshold value is updated, the speech spectrum estimation result is set as a minimum value, and the calculation is as follows:
Figure BDA0001350986280000102
at the same time, the estimated speech spectrum is updated
Figure BDA0001350986280000103
The calculation is as follows:
Figure BDA0001350986280000104
the value range is 0.1-0.3, and the value of the invention is 0.15. If no false detection exists, the current buffer area is indicated to have speech, and the sound signal can be continuously optimized.
The Kalman adaptation enhancement is assumed to use a length LGThe forward prediction filter of (1) predicting the clean speech spectrum, usually LG＜LI. In the present invention, these two parameters are set to L respectivelyG＝15，LI25. Since the speech signal can be well represented by an autoregressive model, the error of prediction can be understood as a reverberation component. Based on the minimum mean square error criterion, the adaptive process of filter update is as follows:
before LIThe frame carries out prediction error vector, prediction vector variance matrix and prediction error initialization, and the initialization process is as follows:
Figure BDA0001350986280000105
Figure BDA0001350986280000106
E(k)＝0
wherein the vector variance matrix P is predictedkIs dimension LG×LG0 matrix of (1) is a prediction error vector GkIs dimension LG0 vector of x 1, and E (k) is the prediction error obtained with the current prediction vector.
From LI+1 frame start, if the voice detection result indicates that there is voice activity, the following adaptive update procedure is performed:
(1.1) updating the prediction error, including the prediction error vector and the prediction spectral error, as follows:
Figure BDA0001350986280000107
Figure BDA0001350986280000108
wherein the content of the first and second substances,
Figure BDA0001350986280000109
is dimension LG×LGThe identity matrix of (2).
(1.2) smoothing the prediction spectrum error to make the error estimation smoother, wherein the specific flow is as follows:
Figure BDA00013509862800001010
E(k)＝η|EPre|2-(1-η)|EPre，o|2
wherein eta is a smoothing coefficient, the value range of the smoothing coefficient is 0.6-0.9, and the value of the method is 0.75.
(1.3) Kalman gain calculation, updating the prediction vector, and updating the process as follows:
Figure BDA00013509862800001011
Figure BDA00013509862800001012
Gk＝G′k+KGEPre
(1.4) reverberation power spectral density update, the update process is as follows:
Figure BDA0001350986280000111
the reverberation power spectral density and the observation signal power spectral density adopt the same smoothing coefficient alpha. Phi'R(k) The reverberant power spectral density of the previous frame. The initial setting of the reverberant power spectral density is 0.
(1.5) constructing an attenuation factor according to the wiener filtering, and outputting an estimated voice spectrum, wherein the calculation is as follows:
Figure BDA0001350986280000112
Figure BDA0001350986280000113
the spectral estimation is used both to recover the time domain signal in the next step and to participate in the computation of the a posteriori signal-to-noise ratio in the first step.
(1.6) circularly executing 1.1-1.5 until all frequency bands are updated, recovering a time domain signal by adopting inverse Fourier transform, wherein the calculation flow is as follows:
Figure BDA0001350986280000114
and after the time domain signal is recovered, sending the time domain signal to a subsequent application terminal, such as a communication device or a voice recognition engine, so as to realize the combined suppression of noise and reverberation.
The method can be used for assisting in voice instruction recognition in a home environment. In a home environment, a user is about 1 to 3 meters away from a microphone, and is affected by home noise and wall reverberation, and the recognition rate is rapidly reduced. The auditory feature extraction method provided by the invention can effectively extract the auditory features in the acquired voice signals, monitor voice activity and reduce the time of false recognition by combining with a corresponding noise removal method. Experiments prove that the recognition rate can be improved from 30% to 65% when the input signal-to-noise ratio is about 10dB at a distance of about 2 meters from a microphone, and the recognition rate is improved from 10% to about 50% when the noise is increased to 20 dB.
The invention provides an auditory feature extraction method for voice activity detection.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the present specification, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (9)

1. An auditory feature extraction method for voice activity detection, comprising the steps of:
acquiring a time domain signal of a sound signal;
calculating a prior signal-to-noise ratio gamma (k) and a posterior signal-to-noise ratio (k) of the sound signal by using the time domain signal, wherein k is a frequency coordinate;
calculating auditory characteristics of the current frame according to the time domain signal, the prior signal-to-noise ratio gamma (k) and the posterior signal-to-noise ratio (k), wherein the auditory characteristics comprise a first dimension parameter, a second dimension parameter and a third dimension parameter; the first dimension parameter is related to the prior signal-to-noise ratio gamma (k), the second dimension parameter is related to the posterior signal-to-noise ratio (k), and the third dimension parameter is related to the time domain signal;
the first dimension parameter is represented by V (1), which is obtained by the following formula:
Figure FDA0002768388220000011
the second dimension parameter is represented by V (2), which is obtained by the following formula:
Figure FDA0002768388220000012
the third dimension parameter is represented by V (3), which is obtained by the following formula:
Figure FDA0002768388220000013
where K is the number of the whole frequency band, LWIs representative of window length, LTRepresenting the starting sample point, the function y is the time domain mixed speech data, and j is the time variable.
2. An auditory feature extraction method for voice activity detection as claimed in claim 1, characterized in that the a priori signal-to-noise ratio γ (k) is found by the following formula:
Figure FDA0002768388220000014
where l is the time frame coordinate, Y (l, k) is the mixed speech spectrum, and Φ V (k) represents the power spectral density of the noise signal.
3. An auditory feature extraction method for voice activity detection according to claim 2, characterized in that the posterior signal-to-noise ratio (k) is found by the following formula:
Figure FDA0002768388220000021
wherein beta is a smoothing factor, beta is a value range of 0.6 to 0.9,
Figure FDA0002768388220000022
to estimate the speech spectrum, the Max function represents the maximum of two variables chosen.
4. An auditory feature extraction method for voice activity detection as claimed in claim 3, characterized in that β is 0.75.
5. An auditory feature extraction method for voice activity detection as claimed in claim 1, characterized in that the time domain signal is represented by y (t), which is represented by the following formula:
Figure FDA0002768388220000023
wherein x (t) is a speech signal with reverberation, v (t) is background noise, h (τ) is a reverberation impulse response signal, and s (t- τ) is a non-reverberation speech signal.
6. The auditory feature extraction method for voice activity detection according to claim 1, characterized in that, before the calculating the prior signal-to-noise ratio γ (k) and the posterior signal-to-noise ratio (k) of the sound signal using the time domain signal, further comprises,
initializing voice parameters including noise power spectral density ΦV(k) Observation signal power spectral density phiY(k) Estimating the speech spectrum
Figure FDA0002768388220000024
A priori signal-to-noise ratio γ (k) and a posteriori signal-to-noise ratio (k), the initialization procedure is as follows:
Figure FDA0002768388220000025
Figure FDA0002768388220000026
Figure FDA0002768388220000031
γ(k)＝1，(k)＝κ，k＝1，2，...，K
wherein K is the number of the whole frequency band, l is the time frame coordinate, Y (l, K) is the mixed voice frequency spectrum, K is the attenuation factor, phi V (K) represents the power spectral density of the noise signal, phi Y (K) represents the power spectral density of the observation signal,
Figure FDA0002768388220000032
to estimate the speech spectrum.
7. An auditory feature extraction method for voice activity detection as claimed in claim 6, further comprising, after the initialization of the voice parameters,
according to the power spectral density of the observation signal of the previous frame, obtaining the power spectral density estimation value of the observation signal of the next frame smoothly, wherein the power spectral density estimation value of the observation signal is obtained by the following formula:
Φ′Y(k)＝αΦY(k)+(1-α)|Y(l，k)|2
wherein alpha is a smoothing factor and has a value range of 0.95-0.995.
8. An auditory feature extraction method for voice activity detection as claimed in claim 7, further comprising, after smoothing the estimate of the observed signal power spectral density for the next frame based on the observed signal power spectral density for the previous frame,
calculating the adaptive updating step length of the noise power spectrum, wherein the adaptive updating step length of the noise power spectrum is obtained by the following formula:
Figure FDA0002768388220000033
wherein the smoothing factor alpha is taken as a fixed step.
9. An auditory feature extraction method for voice activity detection as claimed in claim 8, further comprising, after the step size is adaptively updated by the computation of the noise power spectrum,
updating the noise power spectrum according to the self-adaptive updating step length of the noise power spectrum, wherein the noise power spectrum is obtained by the following formula:
Φv(k)＝αv(k)Φ′v(k)+(1-αv(k))|Y(l，k)|2。
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