Blind multi-channel source separation by circular-linear statistical modeling of phase differences

Johannes Traa, Paris Smaragdis

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

Abstract

We address the problem of blind separation of speech signals with a microphone array. We demonstrate that a signal propagating towards the array at an angle corresponds to interchannel phase difference (IPD) data that lies on a wrapped line (i.e helix) in a circular-linear domain. Thus, the problem reduces to that of fitting helices to data that lies on a cylinder. However, outliers abound because of reverberation, noise, and signal overlap in the time-frequency domain, so we perform the clustering with a sequential variant of Random Sample Consensus (RANSAC). We show that this method can easily be applied to arrays with many microphones and that it is robust in reverberant experimental conditions.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages4320-4324
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • RANSAC
  • blind source separation
  • circular statistics
  • von Mises distribution

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'Blind multi-channel source separation by circular-linear statistical modeling of phase differences'. Together they form a unique fingerprint.

Cite this