Abstract: An acoustic echo cancellation (AEC) system includes a loudspeaker interface coupled to a loudspeaker, a microphone interface coupled to a microphone, and a processor executing a model. The model is 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, 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 an 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.

Abstract: An acoustic echo cancellation (AEC) system includes an adaptive filter, a subtraction circuit, and a processor executing a model. The adaptive filter is arranged to generate an estimated echo signal according to a first microphone signal played by a loudspeaker. The subtraction circuit is arranged to subtract the estimated echo signal from a signal that is output from a microphone receiving both of a speech signal and an echo signal, to generate a second microphone signal, wherein the first microphone signal is not output from the microphone, and the echo signal is transmitted from the loudspeaker to the microphone. The model is arranged to perform short-time Fourier transform upon the first microphone signal and the second microphone signal, respectively, and generate an estimated speech signal through a neural network according to a first transformed microphone signal and a second transformed microphone signal.

Abstract: The acoustic echo cancellation (AEC) system includes a loudspeaker interface coupled to a loudspeaker, a microphone interface coupled to a microphone, and a processor executing a model. The model predicts and generates 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, 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 an 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 the model applies a power function to a loss function of the model according to the true mask and a magnitude of the true mask.