Blind source separation with low frequency compensation for convolutive mixtures

Xiaoming Zhu, Keshab K. Parhi, Warren J. Warwick

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

Abstract

This paper addresses the blind source separation of convolutive and temporally correlated voice mixtures. We combine natural gradient algorithm and temporal complexity algorithm to preserve the temporal and frequency structures of the original signals. Due to the underlying scaling constraint of natural gradient algorithm, the low frequency components of theoriginal sources are suppressed in the output signals. To compensate for low frequency loss, we use a measure of temporal complexity to recover the low frequency components of the source signals. Simulation results show that the proposed algorithm can well preserve the structure of the original signals both in time and frequency domains.

Original languageEnglish (US)
Title of host publicationConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Pages1135-1139
Number of pages5
DOIs
StatePublished - Dec 1 2009
Event43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 1 2009Nov 4 2009

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/1/0911/4/09

Keywords

  • Blind source separation
  • Convolutive mixtures
  • Linear prediction
  • Natural gradient algorithm
  • Temporal complexity

Fingerprint Dive into the research topics of 'Blind source separation with low frequency compensation for convolutive mixtures'. Together they form a unique fingerprint.

Cite this