Abstract
This paper studies the problem of building clusters of music tracks in a collection of popular music in the presence of constraints. The constraints come naturally in the context of music applications. For example, constraints can be generated from the background knowledge (e.g., two artists share similar styles) and the user access patterns (e.g., two pieces of music share similar access patterns across multiple users). We present an approach based on the generalized constraint clustering algorithm by incorporating the constraints for grouping music by "similar" artists. The approach is evaluated on a data set consisting of 53 albums covering 41 popular artists. The "correctness" of the clusters generated is tested using artist similarity provided by All Music Guide.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007 |
| Pages | 27-32 |
| Number of pages | 6 |
| State | Published - 2007 |
| Externally published | Yes |
| Event | 8th International Conference on Music Information Retrieval, ISMIR 2007 - Vienna, Austria Duration: Sep 23 2007 → Sep 27 2007 |
Other
| Other | 8th International Conference on Music Information Retrieval, ISMIR 2007 |
|---|---|
| Country | Austria |
| City | Vienna |
| Period | 9/23/07 → 9/27/07 |
Fingerprint
ASJC Scopus subject areas
- Music
- Information Systems
Cite this
Music clustering with constraints. / Peng, Wei; Li, Tao; Ogihara, Mitsunori.
Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007. 2007. p. 27-32.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Music clustering with constraints
AU - Peng, Wei
AU - Li, Tao
AU - Ogihara, Mitsunori
PY - 2007
Y1 - 2007
N2 - This paper studies the problem of building clusters of music tracks in a collection of popular music in the presence of constraints. The constraints come naturally in the context of music applications. For example, constraints can be generated from the background knowledge (e.g., two artists share similar styles) and the user access patterns (e.g., two pieces of music share similar access patterns across multiple users). We present an approach based on the generalized constraint clustering algorithm by incorporating the constraints for grouping music by "similar" artists. The approach is evaluated on a data set consisting of 53 albums covering 41 popular artists. The "correctness" of the clusters generated is tested using artist similarity provided by All Music Guide.
AB - This paper studies the problem of building clusters of music tracks in a collection of popular music in the presence of constraints. The constraints come naturally in the context of music applications. For example, constraints can be generated from the background knowledge (e.g., two artists share similar styles) and the user access patterns (e.g., two pieces of music share similar access patterns across multiple users). We present an approach based on the generalized constraint clustering algorithm by incorporating the constraints for grouping music by "similar" artists. The approach is evaluated on a data set consisting of 53 albums covering 41 popular artists. The "correctness" of the clusters generated is tested using artist similarity provided by All Music Guide.
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M3 - Conference contribution
AN - SCOPUS:84873606875
SN - 9783854032182
SP - 27
EP - 32
BT - Proceedings of the 8th International Conference on Music Information Retrieval, ISMIR 2007
ER -