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

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arxiv 2305.13686 v1 pith:BYMJTHLO submitted 2023-05-23 eess.AS

Ye-Xin Lu , Yang Ai , Zhen-Hua Ling This is my paper · ORCID
classification eess.AS
keywords spectraphasemagnitudemp-senetdecoderparallelenhancementspeech
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Training-Free Intelligibility-Guided Observation Addition for Noisy ASR

    eess.AS 2026-02 conditional novelty 6.0 of 10

    Mixing noisy and enhanced speech with weights derived from the recognizer's confidence on each signal reduces ASR word error rate without any additional training.

  2. ClaritySpeech: Dementia Obfuscation in Speech

    cs.CL 2025-07 conditional novelty 6.0 of 10

    An ASR, text-obfuscation, and zero-shot TTS pipeline lowers automatic dementia detection in speech by 10 to 16 percent F1 while improving intelligibility, with only moderate speaker similarity.

  3. Evolution Strategy-Based Calibration for Low-Bit Quantization of Speech Models

    cs.SD 2026-03 conditional novelty 5.0 of 10

    Using CMA-ES to jointly optimize activation quantization scales keeps speech-model accuracy near full precision under full INT8 and INT4 quantization.

  4. Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation

    eess.AS 2025-05 conditional novelty 3.0 of 10

    A Transformer-Mamba model that adds a learned correction signal to degraded speech beats adapted active-noise-control baselines on denoising, dereverberation, and declipping in simulation.

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