A Conditional Random Field system for beat tracking

In the present work, we introduce a new probabilistic model for the task of estimating beat positions in a musical audio recording, instantiating the Conditional Random Field (CRF) framework. Our approach takes its strength from a sophisticated temporal modeling of the audio observations, accounting for local tempo variations which are readily represented in the CRF model proposed using well-chosen potentials. The system is experimentally evaluated by studying its performance on 3 datasets of 1394 music excerpts of various western music styles and comparatively to 4 reference systems in the light of 6 reference evaluation metrics. The results show that the proposed system tracks perceptively coherent pulses and is very effective in estimating the beat positions while further work is needed to find the correct salient tempo.