Hidden discrete tempo model: A tempo-aware timing model for audio-to-score alignment

In this paper, we present the Hidden Discrete Tempo Model, an effective Dynamic Bayesian Network for audio to score matching. Its main feature is an explicit modeling of tempo, which directly influences the timing model of the musical performance. Thanks to a discretization of the tempo set, it allows for an efficient decoding by the Viterbi algorithm, and facilitates the introduction of features which directly depend on the local tempo. We take advantage of this property by using the cyclic tempogram descriptor in addition to chroma vectors and onset detection features. Experiment run on both classical piano and pop music show the very high accuracy of this model for audio to score alignment, as well as the usefulness of the tempo feature used.