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

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arxiv 2308.08926 v2 pith:WQRC7NLQ submitted 2023-08-17 eess.AS cs.SD

classification eess.AScs.SD
keywords phasespeechmagnitudespectraenhancementestimationmp-senetarchitecture
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology

    eess.AS 2024-12 conditional novelty 6.0 of 10

    On VoiceBank-DEMAND, replacing dense layers or ReLU activations with GR-KAN layers in MP-SENet and Demucs improved PESQ by up to 0.1 and cut parameters by up to 4x in one comparison.

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