A Unified Approach to Real Time Audio-to-Score and Audio-to-Audio Alignment Using Sequential Montecarlo Inference Techniques

Nicola Montecchio (1) , Arshia Cont (1)

1 STMS - Sciences et Technologies de la Musique et du Son

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.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Nicola Montecchio, Arshia Cont. A Unified Approach to Real Time Audio-to-Score and Audio-to-Audio Alignment Using Sequential Montecarlo Inference Techniques. ICASSP 2011 : Proceedings of International Conference on Acoustics, Speech and Signal Processing, May 2011, Prague, Czech Republic. pp.193-196, ⟨10.1109/ICASSP.2011.5946373⟩. ⟨hal-00692579⟩