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Related Experiment Video

Updated: Jul 13, 2025

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CompNet: Complementary network for single-channel speech enhancement.

Cunhang Fan1, Hongmei Zhang1, Andong Li2

  • 1Anhui Province Key Laboratory of Multimodal Cognitive Computation, School of Computer Science and Technology, Anhui University, Hefei 230601, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces CompNet, a novel speech enhancement network that improves denoising by combining time-domain and frequency-domain processing. It offers insights into why multi-domain approaches outperform single-domain methods for clearer speech.

Keywords:
ComplementaryFiltering and refiningSpeech enhancementTime-domainTime–frequency domain

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Area of Science:

  • Signal Processing
  • Artificial Intelligence
  • Speech Technology

Background:

  • Monaural speech enhancement is crucial for clear audio communication.
  • Multi-domain processing methods show promise but lack clear explanations for their superiority.
  • Existing single-domain approaches have limitations in fully recovering target speech characteristics.

Purpose of the Study:

  • To introduce a complementary single-channel speech enhancement network (CompNet).
  • To provide a novel perspective on the benefits of multi-domain processing in speech enhancement.
  • To improve denoising capabilities and spectral accuracy in monaural speech enhancement.

Main Methods:

  • A dual-path network architecture is proposed for post-processing.
  • The network independently filters magnitude and refines phase in the time-frequency domain.
  • Initial enhancement is performed using a time-domain network, followed by frequency-domain refinement.

Main Results:

  • CompNet demonstrates significant denoising capabilities.
  • The dual-path network effectively refines spectral characteristics, approximating the target spectrum.
  • Experimental results on WSJ0-SI84 and VoiceBank + Demand datasets show competitive performance.

Conclusions:

  • CompNet offers a competitive solution for monaural speech enhancement.
  • The proposed method provides a deeper understanding of multi-domain processing advantages.
  • The network effectively bridges time-domain and frequency-domain processing for improved speech quality.