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
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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
T2 - 12th International Society for Music Information Retrieval Conference, ISMIR 2011
Y2 - 24 October 2011 through 28 October 2011
ER -