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An Information-Maximization Approach to Blind Separation and Blind Deconvolution
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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From Specific default mode subnetworks support mentalizing as revealed through opposing network recruitment by social and semantic FMRI tasks: Mentalizing and Default-Mode Subnetworks (2015) · cites this paper as Bell and Sejnowski, 1995
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Metadata
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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