The DOI matches its OpenAlex record; 2 of the 2 references it cites were found in a registry or an index.
Learning the higher-order structure of a natural sound
Abstract
Unsupervised learning algorithms paying attention only to second-order statistics ignore the phase structure (higher-order statistics) of signals, which contains all the informative temporal and spatial coincidences which we think of as 'features'. Here we discuss how an Independent Component Analysis (ICA) algorithm may be used to elucidate the higher-order structure of natural signals, yielding their independent basis functions. This is illustrated with the ICA transform of the sound of a fingernail tapping musically on a tooth. The resulting independent basis functions look like the sounds themselves, having similar temporal envelopes and the same musical pitches. Thus they reflect both the phase and frequency information inherent in the data.
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Citations
Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Self-citationDaugman, J. G. (1985) · Journal of the Optical Society of America A · doi:10.1364/josaa.2.001160 · Cited by 3,219
Cited by
A Fast Fixed-Point Algorithm for Independent Component Analysis
Hyvärinen, A., & Oja, E. (1997) · Neural Computation · doi:10.1162/neco.1997.9.7.1483 · Cited by 3,391
The “independent components” of natural scenes are edge filters
Bell, A. J., & Sejnowski, T. J. (1997) · Vision Research · doi:10.1016/s0042-6989(97)00121-1 · Cited by 2,201
Self-citationNatural signal statistics and sensory gain control
Schwartz, O., & Simoncelli, E. P. (2001) · Nature Neuroscience · doi:10.1038/90526 · Cited by 835
Natural image statistics and efficient coding
Olshausen, B. A., & Field, D. J. (1996) · Network: Computation in Neural Systems · doi:10.1088/0954-898x_7_2_014 · Cited by 561
Independent component analysis: fetal signal reconstruction from magnetocardiographic recordings
Comani, S., Mantini, D., Pennesi, P., Lagatta, A., & Cancellieri, G. (2004) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2003.12.005 · Cited by 46
Metadata
- Authors
- Bell, A. J., & Sejnowski, T. J.
- Venue
- Network Computation in Neural Systems
- Publisher
- Taylor & Francis
- Published
- 1996-01-01
- Volume
- 7
- Issue
- 2
- Pages
- 261-266
- ISSN
- 0954-898X (ISSN-L)
- OpenAlex
- W1971027050
- Cited by
- 179
- References
- 2
- FWCI
- 13.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bell, A. J., & Sejnowski, T. J. (1996). Learning the higher-order structure of a natural sound. Network Computation in Neural Systems. https://doi.org/10.1088/0954-898x/7/2/005
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
