Robust parameterization of time-frequency characteristics for recognition of musical genres of Mexican culture

Osvaldo G. Pérez Rosas; José L. Rivera Martínez; Luis A. Maldonado Cano; Mario López Rodríguez; Laura M. Amaya Reyes; Elizabeth Cano Martínez; Mireya S. García Vázquez; Alejandro A. Ramírez Acosta

doi:10.1117/12.2274734

19 September 2017 Robust parameterization of time-frequency characteristics for recognition of musical genres of Mexican culture

Osvaldo G. Pérez Rosas, José L. Rivera Martínez, Luis A. Maldonado Cano, Mario López Rodríguez, Laura M. Amaya Reyes, Elizabeth Cano Martínez, Mireya S. García Vázquez, Alejandro A. Ramírez Acosta

Author Affiliations +

Proceedings Volume 10396, Applications of Digital Image Processing XL; 1039635 (2017) https://doi.org/10.1117/12.2274734
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

The automatic identification and classification of musical genres based on the sound similarities to form musical textures, it is a very active investigation area. In this context it has been created recognition systems of musical genres, formed by time-frequency characteristics extraction methods and by classification methods. The selection of this methods are important for a good development in the recognition systems. In this article they are proposed the Mel-Frequency Cepstral Coefficients (MFCC) methods as a characteristic extractor and Support Vector Machines (SVM) as a classifier for our system. The stablished parameters of the MFCC method in the system by our time-frequency analysis, represents the gamma of Mexican culture musical genres in this article. For the precision of a classification system of musical genres it is necessary that the descriptors represent the correct spectrum of each gender; to achieve this we must realize a correct parametrization of the MFCC like the one we present in this article. With the system developed we get satisfactory detection results, where the least identification percentage of musical genres was 66.67% and the one with the most precision was 100%.

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Osvaldo G. Pérez Rosas, José L. Rivera Martínez, Luis A. Maldonado Cano, Mario López Rodríguez, Laura M. Amaya Reyes, Elizabeth Cano Martínez, Mireya S. García Vázquez, and Alejandro A. Ramírez Acosta "Robust parameterization of time-frequency characteristics for recognition of musical genres of Mexican culture", Proc. SPIE 10396, Applications of Digital Image Processing XL, 1039635 (19 September 2017); https://doi.org/10.1117/12.2274734

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