Voice activity detection in transient noise environment using Laplacian pyramid algorithm

Nurit Spingarn, Saman Mousazadeh, Israel Cohen

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

Abstract

Voice activity detection (VAD) has attracted significant research efforts in the last two decades. Despite much progress in designing voice activity detectors, voice activity detection in presence of transient noise and low SNR is a challenging problem. In this paper, we propose a new VAD algorithm based on supervised learning. Our method employs Laplacian pyramid algorithm as a tool for function extension. We estimate the likelihood ratio function of unlabeled data, by extending the likelihood ratios obtained from the labeled data. Simulation results demonstrate the advantages of the proposed method in transient noise environments over conventional statistical methods.

Original languageEnglish
Title of host publication2014 14th International Workshop on Acoustic Signal Enhancement, IWAENC 2014
Pages238-242
Number of pages5
ISBN (Electronic)9781479968084
DOIs
StatePublished - 11 Nov 2014
Event2014 14th International Workshop on Acoustic Signal Enhancement, IWAENC 2014 - Juan-les-Pins, France
Duration: 8 Sep 201411 Sep 2014

Publication series

Name2014 14th International Workshop on Acoustic Signal Enhancement, IWAENC 2014

Conference

Conference2014 14th International Workshop on Acoustic Signal Enhancement, IWAENC 2014
Country/TerritoryFrance
CityJuan-les-Pins
Period8/09/1411/09/14

Keywords

  • Laplacian pyramid algorithm
  • Likelihood ratio function
  • Voice activity detection
  • transient noise

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Acoustics and Ultrasonics
  • Computer Networks and Communications

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