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

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.

Accepted by Interspeech 2023

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• TF-CorrNet: Leveraging Spatial Correlation for Continuous Speech Separation
• CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning
• Study of Lightweight Transformer Architectures for Single-Channel Speech Enhancement
• A Dual-Branch Parallel Network for Speech Enhancement and Restoration