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Music Genre Classification Based on Signal Processing

Music genre is a description, that allows to categorize music compositions into broader categories with similar characteristics. With the development of streaming platforms (iTunes music, SoundCloud, Spotify), the automatic classification of music is becoming increasingly important as a way to intelligently search in a large number of music files, and also as a support in building recommendation systems. In this paper, this approach is based on the extraction of information from a signal (timbre, rhythm, melody, pitch), as well as the construction of high-level features with subsequent classification by methods of machine learning, in particular, the gradient boosting trees and neural networks are considered. The GTZAN dataset is used to evaluate the performance of the algorithms with the best result of 78% precision. Comparison of algorithm with third-party systems is considered. To test algorithms on real data, a website has been developed that allows to automatically classify users’ music.