Blind signal separation with a projection pursuit index
Blind signal separation (BSS) is a powerful technique for separation of mixed signals with weak assumptions on the incoming signals. The objectives of BSS are analogous to the objectives of exploratory projection pursuit which is widely used in the statistical community for finding structure in high...
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| creator | Sarajedini, A. Chau, P.M. |
| description | Blind signal separation (BSS) is a powerful technique for separation of mixed signals with weak assumptions on the incoming signals. The objectives of BSS are analogous to the objectives of exploratory projection pursuit which is widely used in the statistical community for finding structure in high dimensional data sets. In this paper, we adapt exploratory projection pursuit for BSS. First, we introduce exploratory projection pursuit and the associated projection pursuit index (PPI). We adapt the PPI for application to BSS. We also investigate the order of approximation required to achieve satisfactory separation using the PPI, and compare its performance to a maximum-likelihood BSS technique using a Gram-Charlier expansion. |
| doi_str_mv | 10.1109/ICASSP.1998.681565 |
| format | Conference Proceeding |
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No.98CH36181)</title><addtitle>ICASSP</addtitle><description>Blind signal separation (BSS) is a powerful technique for separation of mixed signals with weak assumptions on the incoming signals. The objectives of BSS are analogous to the objectives of exploratory projection pursuit which is widely used in the statistical community for finding structure in high dimensional data sets. In this paper, we adapt exploratory projection pursuit for BSS. First, we introduce exploratory projection pursuit and the associated projection pursuit index (PPI). We adapt the PPI for application to BSS. We also investigate the order of approximation required to achieve satisfactory separation using the PPI, and compare its performance to a maximum-likelihood BSS technique using a Gram-Charlier expansion.</description><subject>Additive white noise</subject><subject>Blind source separation</subject><subject>Covariance matrix</subject><subject>Density measurement</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Equations</subject><subject>Function approximation</subject><subject>Gaussian noise</subject><subject>Gaussian processes</subject><subject>Polynomials</subject><issn>1520-6149</issn><issn>2379-190X</issn><isbn>9780780344280</isbn><isbn>0780344286</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1998</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotT9tKw0AUXLyAoeYH-rQ_kHjObvb2qEWtUFCogm_lxO7qlpiG3RT17w3WYWBgmHOYYWyOUCOCu3pYXK_XTzU6Z2ttUWl1wgohjavQwespK52xMFE2jbBwxgpUAiqNjbtgZc47mNAoBUYVzNx0sd_yHN976nj2AyUa477nX3H84MSHtN_5tz9nOKR8iCOf8v77kp0H6rIv_3XGXu5unxfLavV4P_VbVRGNGCvSIFvwAjFYUhZbI7HVwQX03ls0KAV6uRVWGkvOBu2BtAnCo0ahHMkZmx__xulgM6T4Selnc1wtfwHcw0jB</recordid><startdate>1998</startdate><enddate>1998</enddate><creator>Sarajedini, A.</creator><creator>Chau, P.M.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>1998</creationdate><title>Blind signal separation with a projection pursuit index</title><author>Sarajedini, A. ; Chau, P.M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i172t-a603b0e211f8a581b731b6f9f1eee8171321e3d28378a98f6e0a67f2e161259a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1998</creationdate><topic>Additive white noise</topic><topic>Blind source separation</topic><topic>Covariance matrix</topic><topic>Density measurement</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Equations</topic><topic>Function approximation</topic><topic>Gaussian noise</topic><topic>Gaussian processes</topic><topic>Polynomials</topic><toplevel>online_resources</toplevel><creatorcontrib>Sarajedini, A.</creatorcontrib><creatorcontrib>Chau, P.M.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Sarajedini, A.</au><au>Chau, P.M.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Blind signal separation with a projection pursuit index</atitle><btitle>Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)</btitle><stitle>ICASSP</stitle><date>1998</date><risdate>1998</risdate><volume>4</volume><spage>2125</spage><epage>2128 vol.4</epage><pages>2125-2128 vol.4</pages><issn>1520-6149</issn><eissn>2379-190X</eissn><isbn>9780780344280</isbn><isbn>0780344286</isbn><abstract>Blind signal separation (BSS) is a powerful technique for separation of mixed signals with weak assumptions on the incoming signals. The objectives of BSS are analogous to the objectives of exploratory projection pursuit which is widely used in the statistical community for finding structure in high dimensional data sets. In this paper, we adapt exploratory projection pursuit for BSS. First, we introduce exploratory projection pursuit and the associated projection pursuit index (PPI). We adapt the PPI for application to BSS. We also investigate the order of approximation required to achieve satisfactory separation using the PPI, and compare its performance to a maximum-likelihood BSS technique using a Gram-Charlier expansion.</abstract><pub>IEEE</pub><doi>10.1109/ICASSP.1998.681565</doi></addata></record> |
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| ispartof | Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 1998, Vol.4, p.2125-2128 vol.4 |
| issn | 1520-6149 2379-190X |
| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Additive white noise Blind source separation Covariance matrix Density measurement Eigenvalues and eigenfunctions Equations Function approximation Gaussian noise Gaussian processes Polynomials |
| title | Blind signal separation with a projection pursuit index |
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