A Sparseness - Mixing Matrix Estimation (SMME) solving the underdetermined BSS for convolutive mixtures

Audrey Blin, Shoko Araki, Shoji Makino

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)

Abstract

We propose a method for blindly separating real environment speech signals with as less distortion as possible in the special case where speech signals outnumber sensors. Our idea consists in combining sparseness with the use of an estimated mixing matrix. First, we use a geometrical approach to perform a preliminary separation and to detect when only one source is active. This information is then used to estimate the mixing matrix. Then we remove one source from the observations and separate the residual signals with the inverse of the estimated mixing matrix. Experimental results in a real environment (TR=130ms and 200ms) show that our proposed method, which we call Sparseness - Mixing Matrix Estimation (SMME). provides separated signals of better quality than those extracted by only using the sparseness property of the speech signal.

Original languageEnglish
Pages (from-to)IV-85-IV-88
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
Publication statusPublished - 2004
Externally publishedYes
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 2004 May 172004 May 21

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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