Audio genre classification using percussive pattern clustering combined with timbral features
Emiru Tsunoo, George Tzanetakis, Nobutaka Ono, Shigeki Sagayama · 2009
Many musical genres and styles are characterized by distinct representative rhythmic patterns. In most automatic genre classification systems global statistical features based on timbral dynamics such as mel-frequency cepstral coefficients (MFCC) are utilized but so far rhythmic information has not so effectively been used. In order to extract bar-long unit rhythmic patterns for a music collection we propose a clustering method based on one-pass dynamic programming and k-means clustering. After extracting the fundamental rhythmic patterns for each style/genre a pattern occurrence histogram is calculated and used as a feature vector for supervised learning. Experimental results show that the automatically calculated rhythmic pattern information can be used to effectively classify musical genre/style and improve upon current approaches based on timbral features.