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 Montecarlo 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.

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Nicola Montecchio, Arshia Cont
• Keywords: ICASSP 2011, Real Time, Real Time Alignment, Sequential Montecarlo Inference, Signal Processing