Musical Genre Classification Using SVM and Audio Features

Achmad Benny Mutiara, Rina Refianti, Nadia R.A. Mukarromah

Abstract


The need of advance Music Information Retrieval increases as well as
a huge amount of digital music files distribution on the internet.
Musical genres are the main top-level descriptors used to organize digital music files. Most of work in labeling genre done manually. Thus, an automatic way for labeling a genre to digital music files is needed.
The most standard approach to do automatic musical genre classification is feature extraction followed by supervised machine-learning. This research aims to find the best combination of audio features using several kernels of non-linear Support Vector Machines (SVM). The 31 different  combinations of proposed audio features are dissimilar compared in any other related research. Furthermore, among the proposed audio features, Linear Predictive Coefficients (LPC) has not been used in another works related to musical genre classiffication. LPC was originally used for speech coding. An experimentation in classifying digital music file into a genre is carried out. The experiments are done by extracting feature sets related to timbre, rhythm, tonality and LPC from music files. All possible combination of the extracted features are classified using three different kernel of SVM classifier that are Radial Basis Function (RBF), polynomial and sigmoid.
The result shows that the most appropriate kernel for automatic musical genre classification is polynomial kernel and the best combination of audio features is the combination of musical surface, Mel-Frequency Cepstrum Coefficients (MFFC), tonality and LPC. It achieves 76.6 % in classification accuracy.


Keywords


Support Vector Machine, Audio Features, Mel-Frequency Cepstrum Coefficients, Linear Predictive Coefficients

Full Text:

PDF


DOI: http://dx.doi.org/10.12928/telkomnika.v14i3.3281

Article Metrics

Abstract view : 576 times
PDF - 455 times

Refbacks



Copyright (c) 2019 Universitas Ahmad Dahlan

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

View TELKOMNIKA Stats