Abstract
We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an alternative approach. We employ a (dis)similarity-based learning framework proposed byWang et al. This (dis)similarity-based approach has a theoretical guarantee that one can obtain accurate classifiers using (dis)similarity measures under a natural assumption. We demonstrate the effectiveness of our approach in computational experiments using Japanese MIDI data.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011 |
| Pages | 693-698 |
| Number of pages | 6 |
| Publication status | Published - Dec 1 2011 |
| Event | 12th International Society for Music Information Retrieval Conference, ISMIR 2011 - Miami, FL, United States Duration: Oct 24 2011 → Oct 28 2011 |
Publication series
| Name | Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011 |
|---|
Other
| Other | 12th International Society for Music Information Retrieval Conference, ISMIR 2011 |
|---|---|
| Country | United States |
| City | Miami, FL |
| Period | 10/24/11 → 10/28/11 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Music
- Information Systems
Cite this
Music genre classification using similarity functions. / Anan, Yoko; hatano, kohei; Bannai, Hideo; Takeda, Masayuki.
Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011. 2011. p. 693-698 (Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Music genre classification using similarity functions
AU - Anan, Yoko
AU - hatano, kohei
AU - Bannai, Hideo
AU - Takeda, Masayuki
PY - 2011/12/1
Y1 - 2011/12/1
N2 - We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an alternative approach. We employ a (dis)similarity-based learning framework proposed byWang et al. This (dis)similarity-based approach has a theoretical guarantee that one can obtain accurate classifiers using (dis)similarity measures under a natural assumption. We demonstrate the effectiveness of our approach in computational experiments using Japanese MIDI data.
AB - We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an alternative approach. We employ a (dis)similarity-based learning framework proposed byWang et al. This (dis)similarity-based approach has a theoretical guarantee that one can obtain accurate classifiers using (dis)similarity measures under a natural assumption. We demonstrate the effectiveness of our approach in computational experiments using Japanese MIDI data.
UR - http://www.scopus.com/inward/record.url?scp=84873330630&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873330630&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84873330630
SN - 9780615548654
T3 - Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011
SP - 693
EP - 698
BT - Proceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011
ER -