MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra
Ye-Xin Lu, Yang Ai, Zhen-Hua Ling · 2023
This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel.The proposed MP-SENet adopts a codec architecture in which the encoder and decoder are bridged by convolution-augmented transformers.The encoder aims to encode time-frequency representations from the input noisy magnitude and phase spectra.The decoder is composed of parallel magnitude mask decoder and phase decoder, directly recovering clean magnitude spectra and clean-wrapped phase spectra by incorporating learnable sigmoid activation and parallel phase estimation architecture, respectively.Multi-level losses defined on magnitude spectra, phase spectra, short-time complex spectra, and time-domain waveforms are used to train the MP-SENet model jointly.Experimental results show that our proposed MP-SENet achieves a PESQ of 3.50 on the public VoiceBank+DEMAND dataset and outperforms existing advanced speech enhancement methods.