High–Performance Music Information Retrieval System for Song Genre Classification
| Autor: | Marcin Budka, Amanda C. Schierz |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
business.industry
Computer science Semi-supervised learning computer.software_genre Machine learning CONTEST Automatic indexing Error correcting Music information retrieval Artificial intelligence business Cluster analysis computer Classifier (UML) Natural language processing Coding (social sciences) |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783642219153 ISMIS |
| DOI: | 10.1007/978-3-642-21916-0_76 |
| Popis: | With the large amounts of multimedia data produced, recorded and made available every day, there is a clear need for well-performing automatic indexing and search methods. This paper describes a music genre classification system, which was a winning solution in the Music Information Retrieval ISMIS 2011 contest. The system consisted of a powerful ensemble classifier using the Error Correcting Output Coding coupled with an original, multi-resolution clustering and iterative relabelling scheme. The two approaches used together outperformed other competing solutions by a large margin, reaching the final accuracy close to 88%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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