Individualization of music similarity perception via feature subset selection

We address the problem of modeling the subjective perception of similarity between two music files that have been extracted from a music database with use of objective features. We propose the importation of user models in content-based music retrieval systems, which embody the ability of evolving a...

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Hauptverfasser:	Lampropoulos, A.S., Sotiropoulos, D.N., Tsihrintzis, G.A.
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Applied sciences Audio databases Computer science control theory systems Content based retrieval Control theory. Systems Data mining Exact sciences and technology Feature extraction Feedback Image retrieval Informatics Multiple signal classification Music information retrieval Spatial databases
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creator	Lampropoulos, A.S. Sotiropoulos, D.N. Tsihrintzis, G.A.
description	We address the problem of modeling the subjective perception of similarity between two music files that have been extracted from a music database with use of objective features. We propose the importation of user models in content-based music retrieval systems, which embody the ability of evolving and using different music similarity measures for different users. Specifically, a user-supplied relevance feedback procedure allows the system to determine which subset of a set of objective features approximates more efficiently the subjective music similarity of a specific user. Our implementation of the proposed system verifies our hypothesis and exhibits significant improvement in perceived music similarity
doi_str_mv	10.1109/ICSMC.2004.1398357
format	Conference Proceeding
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Systems</subject><subject>Data mining</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>Feedback</subject><subject>Image retrieval</subject><subject>Informatics</subject><subject>Multiple signal classification</subject><subject>Music information retrieval</subject><subject>Spatial databases</subject><issn>1062-922X</issn><issn>2577-1655</issn><isbn>0780385667</isbn><isbn>9780780385665</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2004</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkEtLAzEUhYMPsK3-Ad1k43LG3LyzlMFHocWFCu5KJnMDkWk7TGYK9ddbreDqLL6Pw-EQcg2sBGDubl69LquSMyZLEM4KZU7IhCtjCtBKnZIpM5YJq7Q2Z2QCTPPCcf5xQaY5fzLGmQQ7Icv5pkm71Iy-TV9-SNsN3Ua6HnMKNKd1an2fhj3tsA_Y_eJd8jSiH8YeaR7rjAPN2GL4gZfkPPo249Vfzsj748Nb9VwsXp7m1f2iSNzAUGAMLoIFOKyOHKxiLhgDNtZCSmmaWgOyxqqo0RtjNNdShxpcEEJ60VgxI7fH3s7n4NvY-01IedX1ae37_QqM4kJad_Bujl5CxH98vEt8A6w5XUE</recordid><startdate>2004</startdate><enddate>2004</enddate><creator>Lampropoulos, A.S.</creator><creator>Sotiropoulos, D.N.</creator><creator>Tsihrintzis, G.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>IQODW</scope></search><sort><creationdate>2004</creationdate><title>Individualization of music similarity perception via feature subset selection</title><author>Lampropoulos, A.S. ; Sotiropoulos, D.N. ; Tsihrintzis, G.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i271t-efc9f1811835f218509c7718fb34447db61e0d85f6ea77762646cb19c334a3d83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Applied sciences</topic><topic>Audio databases</topic><topic>Computer science; control theory; systems</topic><topic>Content based retrieval</topic><topic>Control theory. Systems</topic><topic>Data mining</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>Feedback</topic><topic>Image retrieval</topic><topic>Informatics</topic><topic>Multiple signal classification</topic><topic>Music information retrieval</topic><topic>Spatial databases</topic><toplevel>online_resources</toplevel><creatorcontrib>Lampropoulos, A.S.</creatorcontrib><creatorcontrib>Sotiropoulos, D.N.</creatorcontrib><creatorcontrib>Tsihrintzis, G.A.</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 Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lampropoulos, A.S.</au><au>Sotiropoulos, D.N.</au><au>Tsihrintzis, G.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Individualization of music similarity perception via feature subset selection</atitle><btitle>2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583)</btitle><stitle>ICSMC</stitle><date>2004</date><risdate>2004</risdate><volume>1</volume><spage>552</spage><epage>556 vol.1</epage><pages>552-556 vol.1</pages><issn>1062-922X</issn><eissn>2577-1655</eissn><isbn>0780385667</isbn><isbn>9780780385665</isbn><abstract>We address the problem of modeling the subjective perception of similarity between two music files that have been extracted from a music database with use of objective features. We propose the importation of user models in content-based music retrieval systems, which embody the ability of evolving and using different music similarity measures for different users. Specifically, a user-supplied relevance feedback procedure allows the system to determine which subset of a set of objective features approximates more efficiently the subjective music similarity of a specific user. 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source	IEEE Electronic Library (IEL) Conference Proceedings
subjects	Applied sciences Audio databases Computer science control theory systems Content based retrieval Control theory. Systems Data mining Exact sciences and technology Feature extraction Feedback Image retrieval Informatics Multiple signal classification Music information retrieval Spatial databases
title	Individualization of music similarity perception via feature subset selection
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