The nature of statistical learning theory
The nature of statistical learning theory
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Machine Learning
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Anchor space for classification and similarity measurement of music
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 2
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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The scenarios opened by the increasing availability, sharing and dissemination of music across the Web is pushing for fast, effective and abstract ways of organizing and retrieving music material. Automatic classification is a central activity to model most of these processes, thus its design plays a relevant role in advanced Music Information Retrieval. In this paper, we adopted a state-of-the-art machine learning algorithm, i.e. Support Vector Machines, to design an automatic classifier of music genres. In order to optimize classification accuracy, we implemented some already proposed features and engineered new ones to capture aspects of songs that have been neglected in previous studies. The classification results on two datasets suggest that our model based on very simple features reaches the state-of-art accuracy (on the ISMIR dataset) and very high performance on a music corpus collected locally.