Explicit Estimation of Magnitude and Phase Spectra in Parallel for High-Quality Speech Enhancement

17 Aug 2023  ยท  Ye-Xin Lu, Yang Ai, Zhen-Hua Ling ยท

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network that explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet comprises a Transformer-embedded encoder-decoder architecture. The encoder aims to encode the input distorted magnitude and phase spectra into time-frequency representations, which are further fed into time-frequency Transformers for alternatively capturing time and frequency dependencies. The decoder comprises a magnitude mask decoder and a phase decoder, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude masking architecture and a phase parallel estimation architecture, respectively. Multi-level loss functions explicitly defined on the magnitude spectra, wrapped phase spectra, and short-time complex spectra are adopted to jointly train the MP-SENet model. A metric discriminator is further employed to compensate for the incomplete correlation between these losses and human auditory perception. Experimental results demonstrate that our proposed MP-SENet achieves state-of-the-art performance across multiple speech enhancement tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it further mitigates the compensation effect between the magnitude and phase by explicit phase estimation, elevating the perceptual quality of enhanced speech.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Speech Enhancement Deep Noise Suppression (DNS) Challenge MP-SENet SI-SDR-WB 21.03 # 1
PESQ-NB 3.92 # 1
PESQ-WB 3.62 # 1
Speech Enhancement VoiceBank + DEMAND MP-SENet PESQ 3.60 # 1
CSIG 4.81 # 1
CBAK 3.99 # 1
COVL 4.34 # 1
STOI 0.96 # 8
Para. (M) 2.26 # 6

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