Blind source separation of convolutive mixtures

Shoji Makino*

*Corresponding author for this work

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

4 Citations (Scopus)


This paper introduces the blind source separation (BSS) of convolutive mixtures of acoustic signals, especially speech. A statistical and computational technique, called independent component analysis (ICA), is examined. By achieving nonlinear decorrelation, nonstationary decorrelation, or time-delayed decorrelation, we can find source signals only from observed mixed signals. Particular attention is paid to the physical interpretation of BSS from the acoustical signal processing point of view. Frequency-domain BSS is shown to be equivalent to two sets of frequency domain adaptive microphone arrays, i.e., adaptive beamformers (ABFs). Although BSS can reduce reverberant sounds to some extent in the same way as ABF, it mainly removes the sounds from the jammer direction. This is why BSS has difficulties with long reverberation in the real world. If sources are not "independent," the dependence results in bias noise when obtaining the correct separation filter coefficients. Therefore, the performance of BSS is limited by that of ABF. Although BSS is upper bounded by ABF, BSS has a strong advantage over ABF. BSS can be regarded as an intelligent version of ABF in the sense that it can adapt without any information on the array manifold or the target direction, and sources can be simultaneously active in BSS.

Original languageEnglish
Title of host publicationIndependent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Publication statusPublished - 2006
Externally publishedYes
EventIndependent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV - Kissimmee, FL, United States
Duration: 2006 Apr 172006 Apr 21

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceIndependent Component Analyses, Wavelets, Unsupervised Smart Sensors, and Neural Networks IV
Country/TerritoryUnited States
CityKissimmee, FL


  • Adaptive beamformers
  • Blind source separation
  • Convolutive mixtures
  • Independent component analysis
  • Microphone array

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


Dive into the research topics of 'Blind source separation of convolutive mixtures'. Together they form a unique fingerprint.

Cite this