Downbeat prediction by listening and learning

The perception of downbeat is not yet very well understood, although it generally "feels" natural and intuitive to music listeners. We propose an unbiased and predictive modeling strategy of downbeat by combining psychoacoustic models of music listening with time-lag embedded segment learning. The model is causal, tempo independent, and could inform in a top-down manner a bottom-up beat tracker. Results with particularly complex music examples are presented