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PresentationOpen Access (License Unspecified), Open Access
Conference presentation
Content-Based Music Genre Classification Using Sparse Approximation Techniques
06 Mar 2013
Abstract
In this study we evaluated the performance of genre classification systems using various feature vectors and learning methods. Using a fixed classifier, i.e., the Gaussian mixture models we were able to create a suboptimal feature vector to characterize the audio signals in a low dimensional feature space. We then utilized this modified feature representation to solve the problem of music genre classification. We evaluated the performance of the recent sparsity-eager support vector machines classifier using the proposed feature vector and compared the results to the classic support vector machines and Gaussian mixture models as the baseline classifiers.
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Details
- Title
- Content-Based Music Genre Classification Using Sparse Approximation Techniques
- Creators
- Kamelia Aryafar - Drexel University, Computer ScienceTrevor R. Adams - Drexel University, Computer ScienceAli Shokoufandeh - Drexel University, Computer Science
- Conference
- Drexel IEEE Graduate Forum's Fifth Annual Research Symposium (Philadelphia, Pennsylvania, 06 Mar 2013)
- Resource Type
- Conference presentation
- Language
- English
- Academic Unit
- Computer Science
- Other Identifier
- 991014632345504721