Music Genre Detection using the Naïve Bayes Classifier
Name
Anastassia Semjonova
Abstract
Music in digital form is widely spread nowadays. Musical pieces can be grouped into genres according to
their sounding characteristics. For most people classi cation of a given composition is a reasonably easy task. Automating this classi cation process is, however, not so trivial. Fortunately, we can state the task of digital music classi cation as a machine learning problem. We consider a set of musical compositions with manually assigned genres as a training set and use it to devise an automatic genre classifier.
The traditional approach requires us rstly to extract meaningful features from the acoustic signals, and then apply a general-purpose machine learning algorithm on the transformed data. For feature extraction we used the ideas proposed in the paper by G. Tzanetakis,G. Essl and P. Cook. In their research the authors propose some features that represent music surface and rhythmic structure of audio signals. We reevaluated these features on our own dataset and
suggested some additions. Finally, we selected the best performing feature set combining both the original features and our proposed additions. As long as features are selected properly, the choice of the algorithm is largely irrelevant. In this work we selected the Naïve Bayes algorithm due to its conceptual simplicity and eficiency. as a result of our work, we constructed new feature set of 13 elements that classifi es music of six genres with the accuracy of 61,6% that is almost four times better than random.
Graduation Thesis language
English
Graduation Thesis type
Bachelor - Information Technology
Supervisor(s)
Konstantin Tretjakov
Defence year
2009