Cited by 8,986Blind Source Separation TechniquesNeural Networks and ApplicationsSpeech and Audio Processing

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An Information-Maximization Approach to Blind Separation and Blind Deconvolution

Bell, Anthony J., Sejnowski, Terrence J.·1995·Neural Computation

DOI 10.1162/neco.1995.7.6.1129

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Abstract

We derive a new self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units. The algorithm does not assume any knowledge of the input distributions, and is defined here for the zero-noise limit. Under these conditions, information maximization has extra properties not found in the linear case (Linsker 1989). The nonlinearities in the transfer function are able to pick up higher-order moments of the input distributions and perform something akin to true redundancy reduction between units in the output representation. This enables the network to separate statistically independent components in the inputs: a higher-order generalization of principal components analysis. We apply the network to the source separation (or cocktail party) problem, successfully separating unknown mixtures of up to 10 speakers. We also show that a variant on the network architecture is able to perform blind deconvolution (cancellation of unknown echoes and reverberation in a speech signal). Finally, we derive dependencies of information transfer on time delays. We suggest that information maximization provides a unifying framework for problems in "blind" signal processing.

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Authors: Bell, A. J., & Sejnowski, T. J.
Affiliations: Howard Hughes Medical Institute (US)
Howard Hughes Medical Institute, Computational Neurobiology Laboratory, The Salk Institute, 10010 N. Torrey Pines Road, La Jolla, CA 92037 USA and Department of Biology, University of California, San Diego, La Jolla, CA 92093 USA
ORCID: Bell, Anthony J.
Venue: Neural Computation
Publisher: MIT Press
Published: 1995-11
Volume: 7
Issue: 6
Pages: 1129-1159
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco.1995.7.6.1129
OpenAlex: W2108384452
Cited by: 8,986
References: 52
Citations by year: 2026: 151 · 2025: 242 · 2024: 264 · 2023: 286 · 2022: 329 · 2021: 375 · 2020: 349 · 2019: 372 · 2018: 343 · 2017: 334 · 2016: 346 · 2015: 357 · 2014: 365 · 2013: 352 · 2012: 328
FWCI: 130.90
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
PubMed (publishedVersion)
Funders: Office of Naval Research
Topics: Physical Sciences › Computer Science › Signal Processing › Blind Source Separation Techniques
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords: information maximization, blind source separation, blind deconvolution, self-organized learning, nonlinear neural networks, higher-order statistics, redundancy reduction, principal component analysis, cocktail party problem, time delay dependence, blind signal processing
MeSH terms: Algorithms, Humans, Learning, Neurons, Probability, Problem Solving, Speech, Models, Statistical, Neural Networks, Computer

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An Information-Maximization Approach to Blind Separation and Blind Deconvolution

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