| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Sao Paulo, Inst Fis Sao Carlos, BR-13560970 Sao Paulo - Brazil
[2] Univ Fed Sao Carlos, Dept Computacao, BR-13565905 Sao Paulo - Brazil
[3] Natl Inst Sci & Technol Complex Syst, BR-24210346 Niteroi, RJ - Brazil
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | NEW JOURNAL OF PHYSICS; v. 12, MAY 20 2010. |
| Web of Science Citations: | 11 |
| Abstract | |
Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method. (AU) | |
| FAPESP's process: | 05/00587-5 - Mesh (graph) modeling and techniques of pattern recognition: structure, dynamics and applications |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |