Audio based genre classification of electronic music
Priit Kirss · Jyväskylä University Digital Archive (University of Jyväskylä) · 2007
This thesis aims at developing the audio based genre classification techniques combining some of the existing computational methods with models that are capable of detecting rhythm patterns. The overviews of the features and machine learning algorithms used for current approach are presented. The total 250 musical excerpts from five different electronic music genres such as deep house, techno, uplifting trance, drum and bass and ambient were used for evaluation. The methodology consists of two main steps, first, the feature data is extracted from audio excerpts, and second, the feature data is used to train the machine learning algorithms for classification. The experiments carried out using feature set composed of Rhythm Patterns, Statistical Spectrum Descriptors from RPextract and features from Marsyas gave the highest results. Training that feature set on Support Vector Machine algorithm the classification