Investigation of network architecture for single-channel end-to-end denoising

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper examines the effectiveness of a fully convolutional time-domain audio separation network (Conv-TasNet) on single-channel denoising. Conv-TasNet, which has a structure to explicitly estimate a mask for encoded features, has shown to be effective in single-channel sound source separation in noise-free environments, but it has not been applied to denoising. Therefore, the present study investigates a method of learning Conv-TasNet for denoising and clarifies the optimal structure for single-channel end-to-end modeling. Experimental comparisons conducted using the CHiME-3 dataset demonstrate that Conv-TasNet performs well in denoising and yields improvements in single-channel end-to-end denoising over existing denoising autoencoder-based modeling.

Original languageEnglish
Title of host publication28th European Signal Processing Conference, EUSIPCO 2020 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages441-445
Number of pages5
ISBN (Electronic)9789082797053
DOIs
Publication statusPublished - 2021 Jan 24
Event28th European Signal Processing Conference, EUSIPCO 2020 - Amsterdam, Netherlands
Duration: 2020 Aug 242020 Aug 28

Publication series

NameEuropean Signal Processing Conference
Volume2021-January
ISSN (Print)2219-5491

Conference

Conference28th European Signal Processing Conference, EUSIPCO 2020
Country/TerritoryNetherlands
CityAmsterdam
Period20/8/2420/8/28

Keywords

  • End-to-end modeling
  • Fully convolutional time-domain audio separation network
  • Single-channel denoising
  • Speech recognition
  • Time-domain convolutional denoising autoencoders

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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