Evaluating Low-Level Features for Beat Classification and Tracking

In this paper, we address the question of which low-level acoustical features are the most suitable for identifying music beats computationally. We consider 172 features computed on consecutive signal frames and systematically evaluate their individual value in the task of providing reliable cues for the presence and localisation of beats in music signals. We compare two ways of evaluating features: their accuracy in a song-specific classification task (classifying beats vs nonbeats) and their performance as a front-end to a beat tracking system.