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LOW RESOURCES ONLINE SINGLE-MICROPHONE SPEECH ENHANCEMENT WITH HARMONIC EMPHASIS

Nir Raviv, Ofer Schwartz, Sharon Gannot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, we propose a deep neural network (DNN)-based single-microphone speech enhancement algorithm characterized by a short latency and low computational resources. Many speech enhancement algorithms suffer from low noise reduction capabilities between pitch harmonics, and in severe cases, the harmonic structure may even be lost. Recognizing this drawback, we propose a new weighted loss that emphasizes pitch-dominated frequency bands. For that, we propose a method, applied only at the training stage, to detect these frequency bands. The proposed method is applied to speech signals contaminated by several noise types, and in particular, typical domestic noise drawn from ESC-50 and DEMAND databases, demonstrating its applicability to 'stay-at-home' scenarios.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8807-8811
Number of pages5
ISBN (Electronic)9781665405409
DOIs
StatePublished - 2022
Event2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, Singapore
Duration: 22 May 202227 May 2022

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2022-May

Conference

Conference2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Country/TerritorySingapore
CityHybrid
Period22/05/2227/05/22

Keywords

  • DNN
  • Ideal ratio mask
  • Single-microphone speech enhancement
  • Speech harmonics presence detection

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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