Underdetermined source separation for colored sources

Stefan Winter*, Walter Kellermann, Hiroshi Sawada, Shoji Makino

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

This contribution focuses on the source separation stage as important part of underdetermined blind source separation (BSS). So far nearly all approaches for underdetermined BSS assume independently, identically distributed (i.i.d.) sources. They completely ignore the redundancy that is in the temporal structure of colored sources like speech signals. Instead, we propose multivariate models based on the multivariate Student's t or multivariate Gaussian distribution and investigate their potential for underdetermined BSS. We provide a simple yet effective filter based on the sources' autocorrelations for recovering the sources as basis for further advances in underdetermined BSS. The challenge is estimating the filter coefficients blindly. The experimental results support the idea that source separation for underdetermined BSS can be reduced to the separation of their autocorrelations.

Original languageEnglish
JournalEuropean Signal Processing Conference
Publication statusPublished - 2006
Externally publishedYes
Event14th European Signal Processing Conference, EUSIPCO 2006 - Florence, Italy
Duration: 2006 Sep 42006 Sep 8

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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