TY - GEN
T1 - Improving automatic music tag annotation using stacked generalization of probabilistic SVM outputs
AU - Ness, Steven R.
AU - Theocharis, Anthony
AU - Tzanetakis, George
AU - Martins, Luis Gustavo
N1 - Copyright:
Copyright 2010 Elsevier B.V., All rights reserved.
PY - 2009
Y1 - 2009
N2 - Music listeners frequently use words to describe music. Personalized music recommendation systems such as Last.fm and Pandora rely on manual annotations (tags) as a mechanism for querying and navigating large music collections. A well-known issue in such recommendation systems is known as the cold-start problem: it is not possible to recommend new songs/tracks until those songs/tracks have been manually annotated. Automatic tag annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. We describe how stacked generalization can be used to improve the performance of a state-of-the-art automatic tag annotation system for music based on audio content analysis and report results on two publicly available datasets.
AB - Music listeners frequently use words to describe music. Personalized music recommendation systems such as Last.fm and Pandora rely on manual annotations (tags) as a mechanism for querying and navigating large music collections. A well-known issue in such recommendation systems is known as the cold-start problem: it is not possible to recommend new songs/tracks until those songs/tracks have been manually annotated. Automatic tag annotation based on content analysis is a potential solution to this problem and has recently been gaining attention. We describe how stacked generalization can be used to improve the performance of a state-of-the-art automatic tag annotation system for music based on audio content analysis and report results on two publicly available datasets.
KW - Folksonomies
KW - Music information retrieval
KW - Music recommendation
KW - Sound analysis
KW - Tags
UR - http://www.scopus.com/inward/record.url?scp=72449148704&partnerID=8YFLogxK
U2 - 10.1145/1631272.1631393
DO - 10.1145/1631272.1631393
M3 - Conference contribution
AN - SCOPUS:72449148704
SN - 9781605586083
T3 - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
SP - 705
EP - 708
BT - MM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
T2 - 17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
Y2 - 19 October 2009 through 24 October 2009
ER -