Abstract
Social tags are receiving growing interests in information retrieval. In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering. This paper studies the problem of combining tags and audio contents for artistic style clustering. After studying the effectiveness of using tags and audio contents separately for clustering, this paper proposes a novel language model that makes use of both data sources. Experiments with various methods for combining feature sets demonstrate that tag features are more useful than audio content features for style clustering and that the proposed model can marginally improve clustering performance by combing tags and audio contents.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010 |
| Pages | 57-62 |
| Number of pages | 6 |
| State | Published - 2010 |
| Event | 11th International Society for Music Information Retrieval Conference, ISMIR 2010 - Utrecht, Netherlands Duration: Aug 9 2010 → Aug 13 2010 |
Other
| Other | 11th International Society for Music Information Retrieval Conference, ISMIR 2010 |
|---|---|
| Country | Netherlands |
| City | Utrecht |
| Period | 8/9/10 → 8/13/10 |
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ASJC Scopus subject areas
- Music
- Information Systems
Cite this
Are tags better than audio features? The effect of joint use of tags and audio content features for artistic style clustering. / Wang, Dingding; Li, Tao; Ogihara, Mitsunori.
Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010. 2010. p. 57-62.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Are tags better than audio features? The effect of joint use of tags and audio content features for artistic style clustering
AU - Wang, Dingding
AU - Li, Tao
AU - Ogihara, Mitsunori
PY - 2010
Y1 - 2010
N2 - Social tags are receiving growing interests in information retrieval. In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering. This paper studies the problem of combining tags and audio contents for artistic style clustering. After studying the effectiveness of using tags and audio contents separately for clustering, this paper proposes a novel language model that makes use of both data sources. Experiments with various methods for combining feature sets demonstrate that tag features are more useful than audio content features for style clustering and that the proposed model can marginally improve clustering performance by combing tags and audio contents.
AB - Social tags are receiving growing interests in information retrieval. In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering. This paper studies the problem of combining tags and audio contents for artistic style clustering. After studying the effectiveness of using tags and audio contents separately for clustering, this paper proposes a novel language model that makes use of both data sources. Experiments with various methods for combining feature sets demonstrate that tag features are more useful than audio content features for style clustering and that the proposed model can marginally improve clustering performance by combing tags and audio contents.
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M3 - Conference contribution
AN - SCOPUS:84873586245
SN - 9789039353813
SP - 57
EP - 62
BT - Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010
ER -