Natural gradient multichannel blind deconvolution and source separation using causal FIR filters

Scott C. Douglas, Hiroshi Sawada, Shoji Makino

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

Practical gradient-based adaptive algorithms for multichannel blind deconvolution and convolutive blind source separation typically employ FIR filters for the separation system. Inadequate use of signal truncation within these algorithms can introduce steady-state biases into their converged solutions that lead to degraded separation and deconvolution performances. In this paper, we derive a natural gradient multichannel blind deconvolution and source separation algorithm that mitigates these effects for estimating causal FIR solutions to these tasks. Numerical experiments verify the robust convergence performance of the new method both in multichannel blind deconvolution tasks for i.i.d. sources and in convolutive BSS tasks for acoustic sources, even for extremely-short separation filters.

Original languageEnglish
Pages (from-to)V-477-V-480
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
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

Fingerprint Dive into the research topics of 'Natural gradient multichannel blind deconvolution and source separation using causal FIR filters'. Together they form a unique fingerprint.

Cite this