Experiments in Automatic Genre Classification of Full-length Music Tracks using Audio Activity Rate
The activity rate of an audio clip in terms of three defined attributes results in a generic, quantitative measure of various acoustic sources present in it. The objective of this work is to verify if the acoustic structure measured in terms of these three attributes can be used for genre classification of music tracks. For this, we experiment on classification of full-length music tracks by using a dynamic time warping approach for time-series similarity (derived from the activity rate measure) and also a Hidden Markov Model based classifier. The performance of directly using timbral (Mel-frequency Cepstral Coefficients) features is also presented. Using only the activity rate measure we obtain classification performance that is about 35% better than baseline chance and this compares well with other proposed systems that use musical information such as beat histogram or pitch based melody information.