TY - GEN
T1 - Robust parameterization of time-frequency characteristics for recognition of musical genres of Mexican culture
AU - Pérez Rosas, Osvaldo G.
AU - Rivera Martínez, José L.
AU - Maldonado Cano, Luis A.
AU - López Rodríguez, Mario
AU - Amaya Reyes, Laura M.
AU - Cano Martínez, Elizabeth
AU - García Vázquez, Mireya S.
AU - Ramírez Acosta, Alejandro A.
N1 - Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - 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%.
AB - 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%.
KW - Classification
KW - Classifier
KW - Extractor
KW - MFCC
KW - Musical
KW - Recognition
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85034732839&partnerID=8YFLogxK
U2 - 10.1117/12.2274734
DO - 10.1117/12.2274734
M3 - Contribución a la conferencia
AN - SCOPUS:85034732839
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Applications of Digital Image Processing XL
A2 - Tescher, Andrew G.
PB - SPIE
T2 - Applications of Digital Image Processing XL 2017
Y2 - 7 August 2017 through 10 August 2017
ER -