| Título |
Music genre classification: A semi-supervised approach |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 5th Mexican Conference on Pattern Recognition, MCPR 2013 |
| Resumen |
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 |
| Observaciones |
(including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Code 99454
|
| Lugar |
Querétaro |
| País |
Mexico |
| No. de páginas |
254-263 |
| Vol. / Cap. |
7914 |
| Inicio |
2013-06-26 |
| Fin |
2013-06-29 |
| ISBN/ISSN |
978-364238988-7 |
