Clustering and suppression of transient noise in speech signals using diffusion maps

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

Abstract

Recently we have presented a novel approach for transient noise reduction that relies on non-local (NL) filtering. In this paper, we modify and extend our approach to support clustering and suppression of a few transient noise types simultaneously, by introducing two novel concepts. We observe that voiced speech spectral components are slowly varying compared to transient noise. Thus, by applying an algorithm for noise power spectral density (PSD) estimation, configured to track faster variations than pseudo-stationary noise, the PSD of speech components may be estimated. In addition, we utilize diffusion maps to embed the measurements into a new domain. We obtain a new representation which enables clustering of different transient noise types. The new representation is incorporated into a NL filter as a better affinity metric for averaging over transient instances. Experimental results show that the proposed algorithm enables clustering and suppression of multiple transient interferences.

Original languageEnglish
Title of host publication2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
Pages5084-5087
Number of pages4
DOIs
StatePublished - 2011
Event36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Prague, Czech Republic
Duration: 22 May 201127 May 2011

Publication series

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

Conference

Conference36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Country/TerritoryCzech Republic
CityPrague
Period22/05/1127/05/11

Keywords

  • Speech enhancement
  • acoustic noise
  • impulse noise
  • speech processing
  • transient noise

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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