Automatic Music Genre Classification of Audio Signals with Machine Learning Approaches

Abstract

Musical genre classification is put into context by
explaining about the structures in music and how it is analyzed
and perceived by humans. The increase of the music databases
on the personal collection and the Internet has brought a great
demand for music information retrieval, and especially
automatic musical genre classification. In this research we
focused on combining information from the audio signal than
different sources. This paper presents a comprehensive
machine learning approach to the problem of automatic
musical genre classification using the audio signal. The
proposed approach uses two feature vectors, Support vector
machine classifier with polynomial kernel function and
machine learning algorithms. More specifically, two feature
sets for representing frequency domain, temporal domain,
cepstral domain and modulation frequency domain audio
features are proposed. Using our proposed features SVM act as
strong base learner in AdaBoost, so its performance of the
SVM classifier cannot improve using boosting method. The
final genre classification is obtained from the set of individual
results according to a weighting combination late fusion
method and it outperformed the trained fusion method. Music
genre classification accuracy of 78% and 81% is reported on
the GTZAN dataset over the ten musical genres and the
ISMIR2004 genre dataset over the six musical genres,
respectively. We observed higher classification accuracies with
the ensembles, than with the individual classifiers and
improvements of the performances on the GTZAN and
ISMIR2004 genre datasets are three percent on average. This
ensemble approach show that it is possible to improve the
classification accuracy by using different types of domain
based audio features.