TY - GEN
T1 - Music genre classification
T2 - 5th Mexican Conference on Pattern Recognition, MCPR 2013
AU - Poria, Soujanya
AU - Gelbukh, Alexander
AU - Hussain, Amir
AU - Bandyopadhyay, Sivaji
AU - Howard, Newton
PY - 2013
Y1 - 2013
N2 - Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections.
AB - Music genres can be seen as categorical descriptions used to classify music basing on various characteristics such as instrumentation, pitch, rhythmic structure, and harmonic contents. Automatic music genre classification is important for music retrieval in large music collections on the web. We build a classifier that learns from very few labeled examples plus a large quantity of unlabeled data, and show that our methodology outperforms existing supervised and unsupervised approaches. We also identify salient features useful for music genre classification. We achieve 97.1% accuracy of 10-way classification on real-world audio collections.
UR - http://www.scopus.com/inward/record.url?scp=84888273177&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38989-4_26
DO - 10.1007/978-3-642-38989-4_26
M3 - Contribución a la conferencia
SN - 9783642389887
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 254
EP - 263
BT - Pattern Recognition - 5th Mexican Conference, MCPR 2013, Proceedings
Y2 - 26 June 2013 through 29 June 2013
ER -