CN113393850A - Parameterized auditory filter bank for end-to-end time domain sound source separation system - Google Patents
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Abstract
The invention provides a parameterized auditory filter bank for an end-to-end time domain sound source separation system; a parameterized auditory filter bank is introduced into an end-to-end time domain separation system, a separation model with better auditory rationality is established, and the separation performance of the network is improved. Compared with a fixed filter bank, the parameters of the parameterized auditory filter bank are obtained through network training, so that the method has better flexibility, and can perform spontaneous adjustment according to the characteristics of the network and data to obtain better separation performance. Compared with a free-form filter bank, the parameterized auditory filter bank provides prior information for the network auditory system in a form of a Gamma atom filter, so that the network can better simulate the human auditory system to improve the separation capability of the network in an actual scene, and the network has better interpretability. Furthermore, only 4 parameters per filter need to be trained, which significantly reduces the number of parameters of the network compared to a free-form filter where all parameters need to be trained.
Description
Technical Field
The invention belongs to the field of sound source separation, and particularly relates to a parameterized auditory filter bank for improving the performance of an end-to-end time domain sound source separation system.
Background
In real sound scenes, there is usually simultaneous sounding of multiple sound sources, and sound source separation has always been an important aspect of computational auditory scene analysis. With the rapid development of deep learning, sound source separation systems have made breakthrough progress. As shown in fig. 1, most end-to-end time-domain sound source separation systems at the present stage follow the framework of encoder-separator-decoder. The encoder converts the time-domain mixed sound into an intermediate representation, the separator estimates a weighting function (mask) of the sound source, and then multiplies the mask and the intermediate representation of the mixed sound source and obtains a separated sound source through the decoder.
The encoder refers to a set of filters that convolve a time domain signal, and may be fixed (referred to as a fixed filter bank in the present invention), such as a Short Time Fourier Transform (STFT), a constant Q transform, or an auditory filter bank having a fixed value. On the other hand, the filter bank may also be a set of one-dimensional convolutional layers with any initialization value, and its parameters are obtained through spontaneous learning in the network training process, and is referred to as a free-form filter bank in the present invention.
The fixed filter bank has intuitive explanation and contains priori knowledge, so that overfitting of a network is not easy to cause, but the performance is difficult to improve. In contrast, free-form filter banks have a high degree of freedom, and generally perform better, but are susceptible to noisy data during training.
Disclosure of Invention
Technical problem to be solved
The invention mainly aims at the problems that the performance of a fixed filter bank adopted by a coder in the existing sound source separation system is difficult to improve, and the free filter bank training is easily influenced by noise-containing data, compromises between flexibility and prior information, provides a parameterized auditory filter bank for an end-to-end time domain sound source separation system, and improves the separation performance of a network by improving the coder.
The excellent performance of the human ear in auditory scene analysis inspires us to introduce an auditory filter bank with physiological and psychoacoustic rationality into the sound source separation system. For auditory models, spectral analysis of the cochlea is typically modeled by a Gammatone filter bank. The parametric auditory filter bank proposed in the invention refers to a set of filter banks with a Gamma function form, and the parameters are obtained by network learning, so that the parametric auditory filter bank has better separation performance compared with a fixed filter bank and better auditory rationality and interpretability compared with a free filter bank.
The technical scheme of the invention is as follows:
the parameterized auditory filter bank for the end-to-end time domain sound source separation system adopts Gamma filters, and the number N of the filters is not less than 32; pure tone with filter time domain impulse response modulated by Gamma distribution:
g(t)＝Atp-1e-2πbtcos(2πfct+φ)
wherein p is the order, fcIs the center frequency, b is the bandwidth, phi is the phase, a is the amplitude, determined by the order p and the bandwidth b.
Further, the amplitude is taken as
The method for constructing the end-to-end time domain sound source separation system by using the parameterized auditory filter bank comprises the following steps:
step 1: creating a time-domain separation network according to the framework of the encoder-separator-decoder; the encoder is realized by a one-dimensional convolutional layer, and a filter bank of the encoder is in a parameterized auditory filter bank; a separator for estimating a mask of the sound source; the decoder is a one-dimensional deconvolution layer; multiplying the mask estimation value of each sound source from the separator by the two-dimensional representation of the mixed sound from the encoder, after which the time-domain signals of the separated sound sources can be synthesized by the decoder;
step 2: parameter sets for individual filters based on a priori knowledge of the auditory system of the human ear
Initializing, parameterizing parameter sets in an auditory filter bank
In the network training processThe variable:
(1) order p of each filteriAll are initialized to 4, corresponding to the mean fit value of the filter order in the auditory system of the human ear;
(2) center frequency of each filter
The initialization is uniformly distributed on an equivalent rectangular bandwidth ERB scale, wherein the mapping from linear frequency to ERB scale is
(3) Initialized bandwidth b of each filteriBy
And order piDetermining
(4) Initialization phase phi of each filteriIs set as
To align the peak of the tone with the peak of the Gamma envelope;
and step 3: and selecting different sound sources to create a data set according to the separation task, and training a time domain separation network by using the data set to obtain an end-to-end time domain sound source separation system.
Further, the separator adopts a network structure based on deep convolution, and comprises a plurality of hole convolution modules with different expansion factors, wherein each module comprises a convolution layer, a rectifying layer, a normalization layer, a deep convolution layer and a residue and jump structure.
Further, when the time domain separation network is trained, the dimension invariant signal distortion ratio between the minimum real sound source and the estimated sound source is taken as a training target, the network is trained through an Adam optimizer until the separation performance is not improved any more, and an end-to-end time domain sound source separation system is obtained.
Advantageous effects
The invention introduces a parameterized auditory filter bank into an end-to-end time domain separation system, establishes a separation model with more auditory rationality and improves the separation performance of the network. Compared with a fixed filter bank, the parameters of the parameterized auditory filter bank are obtained through network training, so that the method has better flexibility, and can perform spontaneous adjustment according to the characteristics of the network and data to obtain better separation performance. Compared with a free-form filter bank, the parameterized auditory filter bank provides prior information for the network auditory system in a form of a Gamma atom filter, so that the network can better simulate the human auditory system to improve the separation capability of the network in an actual scene, and the network has better interpretability. Furthermore, only 4 parameters per filter need to be trained, which significantly reduces the number of parameters of the network compared to a free-form filter where all parameters need to be trained.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general frame for a sound source separation system;
FIG. 2 is a diagram of an end-to-end time domain convolution separation network architecture;
FIG. 3(a) the frequency response of a parameterized Gamma Filter Bank, arranged in terms of center frequency; (b) frequency response of 4 gamma filters centered at 1.125 KHz.
Detailed Description
The present invention provides a parameterized auditory filter bank for improving the performance of an end-to-end time domain sound source separation network as an encoder of the end-to-end time domain sound source separation network and creates a more auditory rational end-to-end time domain sound source separation network based on the framework of encoder-separator-decoder. The encoder is in the form of a group of Gamma auditory filters, and the parameters of the encoder are obtained by learning in network training, so that the performance of a separation network is improved, and a foundation is laid for the application of selective listening of a machine in a real scene.
In this embodiment, training an end-to-end time domain separation network for separating any sound source, where an encoder is composed of a set of Gammatone filters, and a parameter set thereof is obtained through network learning, includes the following steps:
step 1: and constructing an end-to-end time domain convolutional network. A network is built according to the framework of encoder-splitter-decoder. The encoder is implemented by means of one-dimensional convolutional layers, the form of which filter bank is given in step 2. The mask used by the splitter to estimate the sound source may have a variety of network forms. The invention provides a network structure based on deep convolution, as shown in fig. 2, which comprises a plurality of hole convolution modules with different expansion factors, wherein each module comprises a convolution layer, a rectifying layer, a normalization layer, a deep convolution layer and a residue and jump structure. In this embodiment, the separator is composed of 3 convolution modules, and each convolution module is implemented by 8 pieces of hole-carrying convolution blocks with an exponent having a spreading factor of 2. The mask estimate from each sound source of the splitter is multiplied by a two-dimensional representation of the mixed sound from the encoder. Finally, the time domain signals of the separated sound sources are synthesized through a decoder (one-dimensional transposition convolution layer).
Step 2: a gamma atone filter bank is created and initialized.
The Gammatone filter is a filter with a good simulation effect on the auditory system, and the time domain impulse response of the filter can be expressed as pure tone modulated by Gamma distribution:
g(t)＝Atp-1e-2πbtcos(2πfct+φ)
wherein p is the order, fcIs the center frequency, b is the bandwidth, phi is the phase, A is the amplitude
The encoder in the invention is a group of Gamma filtersThe number N of filters of the filter is not less than 32. Parameter set of filter
And is variable during the network training process.
Suitable initial values facilitate network training, for which the parameter sets for the individual filters are adapted according to a priori knowledge of the auditory system of the human ear
Initialization is performed.
(1) Order p of each filteriIs initially set to 4, corresponding to the mean fit value of the filter order in the human auditory system.
(2) Center frequency of each filter
The initialization is uniformly distributed on an Equivalent Rectangular Bandwidth (ERB) scale, wherein the mapping from linear frequency to ERB scale is
(3) Initialized bandwidth b of each filteriBy
And order piDetermining
(4) Initialization phase phi of each filteriIs set as
To align the peak of the tone with the peak of the Gamma envelope.
The encoder in this embodiment is composed of 512 Gamma filters of 2ms length, each with a parameter set
The initialization values of (a) are: order pi4, center frequency
Bandwidth b being one of 512 frequency points evenly distributed on the ERB scaleiAnd phase phiiBy a corresponding
And piAnd (4) calculating.
And step 3: a dataset is created and the network is trained. Different sound sources are selected to create the data set according to the separation task. And (3) taking a scale-invariant source-to-distortion ratio (SI-SDR) between the minimized real sound source and the estimated sound source as a training target, training the network by an Adam optimizer until the separation performance is not improved any more, and obtaining a sound source separation model.
To simulate the separation of arbitrary sound sources in a real sound scene, the present embodiment creates a multi-class large data set containing ambient sounds, voices, and tones. Ambient sounds (including traffic noise, sirens, dog calls, etc.) from the BBC effects data set, speech from the Librispeech data set, and musical tones from the musan data set are selected. Each source was down sampled to 16 kHz. Two different sound sources are randomly selected from the data set and mixed with a random signal-to-noise ratio of-5 dB to 5 dB. The data set contained 37.5 hours of acoustic samples, 70% for training, 20% for cross-validation, and 10% for testing.
And (3) taking a scale-invariant source-to-distortion ratio (SI-SDR) between the minimized real sound source and the estimated sound source as a training target, training the network by an Adam optimizer until the separation performance is not improved any more, and obtaining a sound source separation model.
The network SI-SDR improvement values (dB) on the test set are shown in Table 1. Compared with a fixed filter bank, the parameter type Gamma filter bank has the separation performance improved by 2.31dB, and proves that the network can learn the parameter set more suitable for the separation system, and has higher flexibility and better separation performance. Compared with a free-form filter bank, the parametric gamma-atom filter bank not only has better interpretability, but also obtains the improvement on performance, and proves that the gamma-atom filter bank with auditory rationality can introduce prior information beneficial to a separation network.
TABLE 1 Sound Source separation network with different coders SI-SDR improvement values (dB) in test set
Fig. 3(a) shows 512 filter responses obtained after network training, which are arranged by center frequency. (b) The frequency response of 4 gamma filters centered at 1.125KHz is shown. The result shows that the center frequency learned by the network is still distributed according to the ERB scale, but the more abundant orders p and bandwidths b can be learned, which indicates that the network is sensitive to the parameters of the filter. It is difficult to artificially determine suitable fixed parameter values for the filter bank, and spontaneous learning of parameter values through network training is a better way to improve performance.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (6)
1. A parameterized auditory filter bank for an end-to-end time-domain sound source separation system, characterized by: the Gamma filters are adopted, and the number N of the filters is not less than 32; pure tone with filter time domain impulse response modulated by Gamma distribution:
g(t)＝Atp-1e-2πbtcos(2πfct+φ)
wherein p is the order, fcIs the center frequency of the frequency band, and is,b is the bandwidth, phi is the phase, and A is the amplitude, determined by the order p and the bandwidth b.
2. A parameterized acoustic filter bank for an end-to-end time-domain sound source separation system according to claim 1, characterized in that: amplitude is taken as
3. A method of constructing an end-to-end time-domain sound source separation system using the parameterized auditory filter bank of claim 1, wherein: the method comprises the following steps:
step 1: creating a time-domain separation network according to the framework of the encoder-separator-decoder; wherein the encoder is implemented by a one-dimensional convolutional layer, and the filter bank of the encoder is the parameterized auditory filter bank; a separator for estimating a mask of the sound source; the decoder is a one-dimensional deconvolution layer; multiplying the mask estimation value of each sound source from the separator by the two-dimensional representation of the mixed sound from the encoder, after which the time-domain signals of the separated sound sources can be synthesized by the decoder;
step 2: parameter sets for individual filters based on a priori knowledge of the auditory system of the human ear
Initializing, parameterizing parameter sets in an auditory filter bank
During the network training process, variable:
(1) order p of each filteriAll are initialized to 4, corresponding to the mean fit value of the filter order in the auditory system of the human ear;
(2) center frequency of each filter
The initialization is uniformly distributed on the ERB scale of the equivalent rectangular bandwidthFrom linear frequency to ERB scale as
(3) Initialized bandwidth b of each filteriBy
And order piDetermining
(4) Initialization phase phi of each filteriIs set as
To align the peak of the tone with the peak of the Gamma envelope;
and step 3: and selecting different sound sources to create a data set according to the separation task, and training a time domain separation network by using the data set to obtain an end-to-end time domain sound source separation system.
4. A method of constructing an end-to-end time-domain sound source separation system according to claim 3, wherein: the separator adopts a network structure based on deep convolution and comprises a plurality of hole convolution modules with different expansion factors, and each hole convolution module comprises a convolution layer, a rectifying layer, a normalization layer, a deep convolution layer, a residue number and a jump structure.
5. A method of constructing an end-to-end time-domain sound source separation system according to claim 3, wherein: when the time domain separation network is trained, an end-to-end time domain sound source separation system is obtained by taking the minimized scale invariant signal distortion ratio between a real sound source and an estimated sound source as a training target and training the network through an Adam optimizer until the separation performance is not improved any more.
6. An end-to-end time domain sound source separation system, characterized by: constructed by the method of claim 3.
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