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arXiv · 2305.13686

MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra

Ye-Xin Lu·Yang Ai·Zhen-Hua Ling
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Abstract

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.

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BibTeXRIS

Ye-Xin Lu, Yang Ai, Zhen-Hua Ling. 2023-05-23. MP-SENet: A Speech Enhancement Model with Parallel Denoising of Magnitude and Phase Spectra. https://doi.org/10.21437/interspeech.2023-1441

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