Deep learning-based multichannel acoustic echo cancellation
Multichannel sound systems that utilize multi-channel audio playback devices can improve the reality and space of sound,but for hands-free communication,these systems are inevitably influenced by noise and echo,which seriously impair the communication experience.To address this issue,this paper proposes a multichannel acoustic echo cancellation and noise suppression method based on a gated convolutional recurrent neural network.This method takes the compressed complex spectrum of the near-end microphone and that of each far-end loudspeaker signal as the network input,and the compressed complex spectrum of the near-end clean speech as the network output.In this way,we can recover the clean speech from the microphone signal directly.The proposed method does not need to decorrelate the far-end signals,and thus the quality of multichannel sound reproduction is not degraded.Meanwhile,the proposed method solves the non-unique solution problem existing in the conventional adaptive filtering-based methods.Experimental results on both the simulated and real acoustic scenarios show that the proposed method can significantly suppress the noise and echo interferences in the multichannel sound system and outperform other competing methods in terms of the speech quality improvement and the echo reduction amount.
万方数据
维普
