Audio Genre Classification Employing Support Vector Machine

Audio genre classification is recently getting a growing interest in the field of audio processing, genres are basically labels which are created and utilized for categorizing huge amount of universal music. Because of non-stationary and discontinuous nature of the audio signal, segmentation and cla...

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Bibliographic Details
Published in2018 4th International Conference for Convergence in Technology (I2CT) pp. 1 - 5
Main Authors Mali, Gopal, Mahajan, Shrinivas P.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Audio Classification
Feature extraction
Feature vector
Kernel
Mel frequency cepstral coefficient
MFCC
Music
RBF
Support vector machines
SVM
Task analysis
Training
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Summary:Audio genre classification is recently getting a growing interest in the field of audio processing, genres are basically labels which are created and utilized for categorizing huge amount of universal music. Because of non-stationary and discontinuous nature of the audio signal, segmentation and classification have come to be a challenging task. Each genre differs from each other in their timbral, instrumental and rhythmic content of music. This paper gives the details of the genre classification using Support Vector Machine (SVM) and their comparison in terms of accuracy with other classifiers. The proposed classifier gives an accuracy of 92 % when classifying 5 musical genres which are higher compared to other classifiers used in this experiment.
DOI:10.1109/I2CT42659.2018.9058184