Conference proceeding
Automatic Musical Genre Classification Using Sparsity-Eager Support Vector Machines
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), pp.1526-1529
International Conference on Pattern Recognition
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Constructing robust categorical and typological classifiers, i.e., finding auditory constructs utilized for describing music categories, is an important problem in music genre classification. Supervised methods such as support vector machine (SVM) achieve state of the art performance for genre classification but suffer from over-fitting on training examples. In this paper, we introduce a supervised classifier, '1-SVM, that utilizes sparse methods to deal with over-fitting for genre classification. We compare the proposed algorithm to competing learning methods such as SVM, logistic regression, and '1-regression for genre classification. Experimental results suggest that the proposed method using short-time audio features (MFCCs) outperforms the baseline algorithms in terms of the average classification accuracy rate of musical genres.
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Details
- Title
- Automatic Musical Genre Classification Using Sparsity-Eager Support Vector Machines
- Creators
- Kamelia Aryafar - Drexel Univ, Philadelphia, PA 19104 USASina JafarpourAli Shokoufandeh - Drexel Univ, Philadelphia, PA 19104 USAIEEE
- Publication Details
- 2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012), pp.1526-1529
- Series
- International Conference on Pattern Recognition
- Publisher
- IEEE
- Number of pages
- 4
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Identifiers
- 991019170338604721
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- Computer Science, Artificial Intelligence