A new mutual information measure for independent component alalysis

Jen-Tzung Chien*, Hsin Lung Hsieh, Sadaoki Furui

*Corresponding author for this work

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

12 Scopus citations

Abstract

Independent component analysis (ICA) is a popular approach for blind source separation (BSS). In this study, we develop a new mutual information measure for BSS and unsupervised learning of acoustic models. The underlying concept of ICA unsupervised learning algorithm is to demix the observations vectors and identify the corresponding mixture sources. These independent sources represent the specific speaker, gender, accent, noise or environment, etc, embedded in acoustic models. The novelty of the proposed ICA is to derive a new metric of mutual information for measuring the dependence among mixture sources. We focus on building this metric based on the Jensen's inequality, which is illustrated to use smaller number of iterations in finding the demixing matrix compared to other types of mutual information. We present a parametric ICA using the generalized Gaussian distribution to characterize the non-Gaussianity of model parameters. Also, a nonparametric ICA is established by using the Parzen window based distribution. In the experiments on BSS and noisy speech recognition, we demonstrate the effectiveness of the proposed Jensen ICA compared to FastICA and other nonparametric ICA.

Original languageEnglish
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
Pages1817-1820
Number of pages4
DOIs
StatePublished - 16 Sep 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: 31 Mar 20084 Apr 2008

Publication series

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

Conference

Conference2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period31/03/084/04/08

Keywords

  • Independent component analysis
  • JENSEN'S inequality
  • Mutual information
  • Speech recognition

Fingerprint Dive into the research topics of 'A new mutual information measure for independent component alalysis'. Together they form a unique fingerprint.

Cite this