Supervised system identification based on local PCA models

Tomer Koren, Ronen Talmon, Israel Cohen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a supervised system identification method for recovering an acoustic impulse response in a reverberant room. Unlike most existing methods, our algorithm is based on prior information given in the form of a training set of known impulse responses acquired in a controlled environment. By relying on the prior information, we train local Principal Component Analysis (PCA) models of impulse responses corresponding to several different regions in the room. We propose to crudely localize the respective source position, and subsequently, based on the appropriate local model, recover the impulse response. In order to approximate the source location, we introduce a specially-tailored distance measure which is based on an affinity between the trained local models. Experimental results in simulated noisy and reverberant environments demonstrate significant improvements over existing methods.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages541-544
Number of pages4
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • System identification
  • acoustic source localization
  • local PCA
  • principal component analysis

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