Underdetermined Blind Source Separation Based on Subspace Representation
This paper considers the problem of blindly separating sub- and super-Gaussian sources from underdetermined mixtures. The underlying sources are assumed to be composed of two orthogonal components: one lying in the rowspace and the other in the nullspace of a mixing matrix. The mapping from the rows...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers
2010
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| Online Access: | http://hdl.handle.net/1721.1/51862 |
| _version_ | 1811080273787355136 |
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| author | Kim, Sanggyun Yoo, Chang D. |
| author2 | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Kim, Sanggyun Yoo, Chang D. |
| author_sort | Kim, Sanggyun |
| collection | MIT |
| description | This paper considers the problem of blindly separating sub- and super-Gaussian sources from underdetermined mixtures. The underlying sources are assumed to be composed of two orthogonal components: one lying in the rowspace and the other in the nullspace of a mixing matrix. The mapping from the rowspace component to the mixtures by the mixing matrix is invertible using the pseudo-inverse of the mixing matrix. The mapping from the nullspace component to zero by the mixing matrix is noninvertible, and there are infinitely many solutions to the nullspace component. The latent nullspace component, which is of lower complexity than the underlying sources, is estimated based on a mean square error (MSE) criterion. This leads to a source estimator that is optimal in the MSE sense. In order to characterize and model sub- and super-Gaussian source distributions, the parametric generalized Gaussian distribution is used. The distribution parameters are estimated based on the expectation-maximization (EM) algorithm. When the mixing matrix is unavailable, it must be estimated, and a novel algorithm based on a single source detection algorithm, which detects time-frequency regions of single-source-occupancy, is proposed. In our simulations, the proposed algorithm, compared to other conventional algorithms, estimated the mixing matrix with higher accuracy and separated various sources with higher signal-to-interference ratio. |
| first_indexed | 2024-09-23T11:28:35Z |
| format | Article |
| id | mit-1721.1/51862 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:28:35Z |
| publishDate | 2010 |
| publisher | Institute of Electrical and Electronics Engineers |
| record_format | dspace |
| spelling | mit-1721.1/518622022-09-27T19:47:51Z Underdetermined Blind Source Separation Based on Subspace Representation Kim, Sanggyun Yoo, Chang D. Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences Kim , Sanggyun Kim, Sanggyun This paper considers the problem of blindly separating sub- and super-Gaussian sources from underdetermined mixtures. The underlying sources are assumed to be composed of two orthogonal components: one lying in the rowspace and the other in the nullspace of a mixing matrix. The mapping from the rowspace component to the mixtures by the mixing matrix is invertible using the pseudo-inverse of the mixing matrix. The mapping from the nullspace component to zero by the mixing matrix is noninvertible, and there are infinitely many solutions to the nullspace component. The latent nullspace component, which is of lower complexity than the underlying sources, is estimated based on a mean square error (MSE) criterion. This leads to a source estimator that is optimal in the MSE sense. In order to characterize and model sub- and super-Gaussian source distributions, the parametric generalized Gaussian distribution is used. The distribution parameters are estimated based on the expectation-maximization (EM) algorithm. When the mixing matrix is unavailable, it must be estimated, and a novel algorithm based on a single source detection algorithm, which detects time-frequency regions of single-source-occupancy, is proposed. In our simulations, the proposed algorithm, compared to other conventional algorithms, estimated the mixing matrix with higher accuracy and separated various sources with higher signal-to-interference ratio. Brain Korea 21 Project Basic Research Program of the Korea Science and Engineering Foundation 2010-03-01T20:12:45Z 2010-03-01T20:12:45Z 2009-06 2009-02 Article http://purl.org/eprint/type/JournalArticle 1053-587X http://hdl.handle.net/1721.1/51862 SangGyun Kim, and C.D. Yoo. “Underdetermined Blind Source Separation Based on Subspace Representation.” Signal Processing, IEEE Transactions on 57.7 (2009): 2604-2614. ©2009 Institute of Electrical and Electronics Engineers. en_US http://dx.doi.org/10.1109/tsp.2009.2017570 IEEE Transactions on Signal Processing : a publication of the IEEE Signal Processing Society Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers IEEE |
| spellingShingle | Kim, Sanggyun Yoo, Chang D. Underdetermined Blind Source Separation Based on Subspace Representation |
| title | Underdetermined Blind Source Separation Based on Subspace Representation |
| title_full | Underdetermined Blind Source Separation Based on Subspace Representation |
| title_fullStr | Underdetermined Blind Source Separation Based on Subspace Representation |
| title_full_unstemmed | Underdetermined Blind Source Separation Based on Subspace Representation |
| title_short | Underdetermined Blind Source Separation Based on Subspace Representation |
| title_sort | underdetermined blind source separation based on subspace representation |
| url | http://hdl.handle.net/1721.1/51862 |
| work_keys_str_mv | AT kimsanggyun underdeterminedblindsourceseparationbasedonsubspacerepresentation AT yoochangd underdeterminedblindsourceseparationbasedonsubspacerepresentation |