Filtering of beamformed speech signals
View Patent ↗The invention relates to speech signal processing that detects a speech signal from more than one microphone and obtains microphone signals that are processed by a beamformer to obtain a beamformed signal that is post-filtered signal with a filter that employs adaptable filter weights to obtain an enhanced beamformed signal with the post-filter adapting the filter weights with previously learned filter weights.
1. A method for speech signal processing, comprising:
detecting a speech signal by more than one microphone to obtain microphone signals;
processing the microphone signals with a beamformer to obtain a beamformed signal; and
post-filtering the beamformed signal by a post-filter that employs adaptable filter weights to obtain an enhanced beamformed signal, where the post-filter adapts the filter weights with previously learned filter weights, where the learned filter weights are obtained by supervised learning, where the supervised learning comprises the steps of:
generating sample signals by superimposing a wanted signal contribution associated with the more than one microphone and a noise contribution for each of the sample signals;
inputting the sample signals, each comprising a wanted signal contribution and a noise contribution, into a beamforming means to obtain beamformed sample signals; and
training filter weights for the post-filterer such that beamformed sample signals filtered by a filter updating module use the trained filter weights to approximate the wanted signal contributions of the sample signals.
2. The method of claim 1 , further including:
extracting at least one feature from the microphone signals;
inputting the at least one extracted feature into a non-linear mapping module;
outputting the previously learned filter weights by the non-linear mapping module in response to the extracted at least one feature; and
adapting the filter weights of the post-filtering module in response to the learned filter weights output by the non-linear mapping module.
3. The method of claim 2 , where the non-linear mapping is performed by a trained neural network.
4. The method of claim 3 , further including:
dividing the microphone signals into microphone sub-band signals;
Mel band filtering the sub-band signals;
extracting at least one feature from the Mel band filtered sub-band signals;
outputting the learned filter weights by the non-linear mapping module as Mel band filter weights; and
processing the Mel band filter weights output by the non-linear mapping module to obtain filter weights in a frequency domain to adapt the filter weights of the post-filter.
5. The method of claim 4 , where the Mel band filter weights output by the non-linear mapping module further include temporal smoothing of the Mel band filter weights.
6. The method of claim 4 , where the at least one feature is the signal power densities of the microphone signals.
7. The method of claim 4 , where the at least one feature is a ratio of the squared magnitude of the sum of two microphone sub-band signals and the squared magnitude of the difference of two microphone sub-band signals.
8. The method of claim 4 , where the at least one feature is an output power density of the normalized average power density of the microphone signals.
9. The method of claim 4 , where the at least one feature is a mean squared coherence of two microphone signals.
10. The method of claim 1 , where the enhanced beamformed signal, X p , is obtained by the post-filter is according to X p =H X BF , where H denotes the adapted filter weights of the post-filter and X BF denotes the beamformed signal.
11. The method of claim 1 , further includes:
beamforming the wanted signal contributions of the sample signals by a fixed beamformer to obtain beamformed wanted signal contributions of the sample signals; and
training filter weights for the post-filtering module such that beamformed sample signals filtered by a filtering updating module where the trained filter weights approximate the beamformed wanted signal contributions of the sample signals.
12. A computer program product for performing speech signal processing to reduce background noise, the computer program product comprising a nontransitory computer readable medium encoded with computer readable program code, the computer readable code including:
program code for detecting a speech signal by more than one microphone to obtain microphone signals;
program code for processing the microphone signals with a beamformer to obtain a beamformed signal; and
program code for post-filtering the beamformed signal by a post-filter that employs adaptable filter weights to obtain an enhanced beamformed signal, where the post-filter adapts the filter weights with previously learned filter weights, where the learned filter weights are obtained by supervised learning, where the supervised learning comprises:
generating sample signals by superimposing a wanted signal contribution associated with the more than one microphone and a noise contribution for each of the sample signals;
inputting the sample signals, each comprising a wanted signal contribution and a noise contribution, into a beamforming means to obtain beamformed sample signals; and
training filter weights for the post-filterer such that beamformed sample signals filtered by a filter updating module use the trained filter weights to approximate the wanted signal contributions of the sample signals.
13. The computer program product according to claim 12 , further including:
program code for extracting at least one feature from the microphone signals;
program code for inputting the at least one extracted feature into a non-linear mapping module;
program code for outputting the previously learned filter weights by the non-linear mapping module in response to the extracted at least one feature; and
program code for adapting the filter weights of the post-filtering module in response to the learned filter weights output by the non-linear mapping module.
14. The computer program product according to claim 13 , where the non-linear mapping is performed by a trained neural network.
15. The computer program product according to claim 14 , further including:
program code for dividing the microphone signals into microphone sub-band signals;
program code for Mel band filtering the sub-band signals;
program code for extracting the at least one feature from the Mel band filtered sub-band signals;
program code for outputting the learned filter weights by the non-linear mapping module as Mel band filter weights; and
program code for processing the Mel band filter weights output by the non-linear mapping module to obtain filter weights in a frequency domain to adapt the filter weights of the post-filter.
16. The computer program product according to claim 15 , where the Mel band filter weights output by the non-linear mapping module further include temporal smoothing of the Mel band filter weights.
17. The computer program product according to claim 15 , where the at least one feature is the signal power densities of the microphone signals.
18. The computer program product according to claim 15 , where the at least one feature is a ratio of the squared magnitude of the sum of two microphone sub-band signals and the squared magnitude of the difference of two microphone sub-band signals.
19. The computer program product according to claim 15 , where the at least one feature is an output power density of the normalized average power density of the microphone signals.
20. The computer program product according to claim 15 , where the at least one feature is a mean squared coherence of two microphone signals.
21. The computer program product according to claim 12 , where the enhanced beamformed signal, X P , is obtained by the post-filter according to X P =H X BF , where H denotes the adapted filter weights of the post-filter and X BF denotes the beamformed signal.