Acoustic echo cancellation system and associated method

1 . An acoustic echo cancellation (AEC) system, comprising:

a loudspeaker interface, coupled to a loudspeaker;

a microphone interface, coupled to a microphone;

a processor, arranged to execute:

a model, arranged to predict and generate a spectral magnitude mask (SMM) through a neural network according to a first microphone signal output by the loudspeaker and a second microphone signal output by the microphone, for generating an estimated speech signal, wherein a noisy speech signal is a sum of a clean speech signal and a noise signal; the SMM is a ratio of a spectral magnitude of the estimated speech signal and a spectral magnitude of the noisy speech signal; a true mask is a ratio of a spectral magnitude of the clean speech signal and the spectral magnitude of the noisy speech signal; and a loss function of the model is a mean square error between the SMM and the true mask;

wherein the processor is further arranged to train the model by minimizing the loss function, for generating a trained model to generate the estimated speech signal;

wherein the SMM comprises a real part mask and an imaginary part mask; the real part mask is a real part of the SMM and the imaginary part mask is an imaginary part of the SMM; and the real part mask corresponds to magnitude information of the first microphone signal and the second microphone signal, the imaginary part mask corresponds to phase information of the first microphone signal and the second microphone signal, and the loss function of the model is a sum of a mean square error between the real part mask and a real part of the true mask and a mean square error between the imaginary part mask and an imaginary part of the true mask.

2 . The AEC system of claim 1 , wherein a real part of the estimated speech signal is obtained by multiplying the real part mask by a real part of the second microphone signal, and an imaginary part of the estimated speech signal is obtained by multiplying the imaginary part mask by an imaginary part of the second microphone signal.

3 . The AEC system of claim 1 , wherein the model comprises:

multiple segment modules, arranged to split the first microphone signal and the second microphone signal, respectively, to generate a first segmented microphone signal and a second segmented microphone signal;

multiple fast Fourier transform modules, arranged to perform fast Fourier transform upon the first segmented microphone signal and the second segmented microphone signal, respectively, to generate a first transformed microphone signal and a second transformed microphone signal;

multiple instant layer normalization (iLN) modules, arranged to normalize the first transformed microphone signal and the second transformed microphone signal, respectively, to generate a first normalized microphone signal and a second normalized microphone signal;

a concat module, arranged to concatenate the first normalized microphone signal and the second normalized microphone signal, to generate a concatenated result; and

a separation kernel, arranged to predict and generate the SMM according to the concatenated result, wherein the estimated speech signal is generated according to the SMM.

4 . The AEC system of claim 3 , wherein the separation kernel comprises multiple long short term memory (LSTM) layers and a fully-connected layer with sigmoid activation, and the SMM is predicted and generated by the multiple LSTM layers and the fully-connected layer with sigmoid activation.

5 . An acoustic echo cancellation (AEC) method, comprising:

executing a model to predict and generate a spectral magnitude mask (SMM) through a neural network according to a first microphone signal output by a loudspeaker and a second microphone signal output by a microphone, for generating an estimated speech signal, wherein a noisy speech signal is a sum of a clean speech signal and a noise signal; the SMM is a ratio of a spectral magnitude of the estimated speech signal and a spectral magnitude of the noisy speech signal; a true mask is a ratio of a spectral magnitude of the clean speech signal and the spectral magnitude of the noisy speech signal; and a loss function of the model is a mean square error between the SMM and the true mask;

wherein the method further comprises:

training the model by minimizing the loss function, for generating a trained model to generate the estimated speech signal;

wherein the SMM comprises a real part mask and an imaginary part mask; the real part mask is a real part of the SMM and the imaginary part mask is an imaginary part of the SMM; and the real part mask corresponds to magnitude information of the first microphone signal and the second microphone signal, the imaginary part mask corresponds to phase information of the first microphone signal and the second microphone signal, and the loss function of the model is a sum of a mean square error between the real part mask and a real part of the true mask and a mean square error between the imaginary part mask and an imaginary part of the true mask.

6 . The AEC method of claim 5 , wherein a real part of the estimated speech signal is obtained by multiplying the real part mask by a real part of the second microphone signal, and an imaginary part of the estimated speech signal is obtained by multiplying the imaginary part mask by an imaginary part of the second microphone signal.

7 . The AEC method of claim 5 , wherein the model is further arranged to perform steps of:

splitting the first microphone signal and the second microphone signal, respectively, to generate a first segmented microphone signal and a second segmented microphone signal;

performing fast Fourier transform upon the first segmented microphone signal and the second segmented microphone signal, respectively, to generate a first transformed microphone signal and a second transformed microphone signal;

normalizing the first transformed microphone signal and the second transformed microphone signal, respectively, to generate a first normalized microphone signal and a second normalized microphone signal;

concatenating the first normalized microphone signal and the second normalized microphone signal, to generate a concatenated result; and

predicting and generating the SMM according to the concatenated result, wherein the estimated speech signal is generated according to the SMM.

8 . The AEC method of claim 7 , wherein the step of predicting and generating the SMM according to the concatenated result comprises:

predicting and generating the SMM by multiple long short term memory (LSTM) layers and a fully-connected layer with sigmoid activation.