Abstract:
Digital music is one of the most important
data types, distributed by the Internet. Automatic
musical genre classification is very useful for
music indexing and retrieval. A method to
recognize the genre of music audio is considered.
In this paper, the input music is represented with
DWT (Discrete Wavelet Transform) coefficients
and classifying the extracted features is performed
using Gaussian Mixture Models (GMM). Using
GMM the optimal class boundaries between four
groups of genre namely, pop, classic, rock and jazz
are obtained. The feature vector from feature
extraction step uses wavelet coefficients by
hierarchical decomposition as it is easy to
implement as well as it can reduce the computation
time and resources required. Given that GMM is a
robust approach that could obtain very good
performance and a solution based on it is
powerful, the classification is mainly composed of
GMM classifiers. The experimental results indicate
that the proposed approach offer encouraging
results.
