A unified approach to real time audio-to-score and audio-to-audio alignment using sequential Montecarlo inference techniques

We present a methodology for the real time alignment of music signals using sequential Monte Carlo inference techniques. The alignment problem is formulated as the state tracking of a dynamical system, and differs from traditional Hidden Markov Model Dynamic Time Warping based systems in that the hidden state is continuous rather than discrete. The major contribution of this paper is addressing both problems of audio-to-score and audio-to-audio alignment within the same framework in a real time setting. Performances of the proposed methodology on both problems are then evaluated and discussed.