TY - GEN
T1 - Genre classification for million song dataset using confidence-based classifiers combination
AU - Hu, Yajie
AU - Ogihara, Mitsunori
PY - 2012/9/28
Y1 - 2012/9/28
N2 - We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.
AB - We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.
KW - classifier combination
KW - song genre classification
UR - http://www.scopus.com/inward/record.url?scp=84866596498&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866596498&partnerID=8YFLogxK
U2 - 10.1145/2348283.2348480
DO - 10.1145/2348283.2348480
M3 - Conference contribution
AN - SCOPUS:84866596498
SN - 9781450316583
T3 - SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 1083
EP - 1084
BT - SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
T2 - 35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
Y2 - 12 August 2012 through 16 August 2012
ER -