Musical Genre Classification Using Support Vector Machines and Audio Features
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 classification. LPC was originally used for speech coding. Anexperimentation
in classifying digital music file into a genre is carried out. The experiments
are done byextracting 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 automaticmusical genre classification is polynomial
kernel and the best combination of audio features is the combination of musical
surface, Mel-Frequency Cepstrum Coefficients (MFCC), tonality and LPC. It
achieves 76.6 % in classification accuracy.
Keyword: Support Vector
Machine, Audio Features, Mel-Frequency Cepstrum Coefficients, Linear Predictive
Coefficients
Author: A.B. Mutiara*, R.
Refianti , and N.R.A. Mukarromah
Journal Code: jptkomputergg160266