Robust on-line algorithm for real-time audio-to-score alignment based on a delayed decision and anticipation framework

In this paper, we present a robust on-line algorithm for real-time audio-to-score alignment based on a delayed decision and anticipation framework. We employ Segmental Conditional Random Fields and Linear Dynamical System to model musical performance. The combination of these models allows an efficient iterative decoding of score position and tempo. The combined advantages of our approach are the delayed-decision Viterbi algorithm which utilizes future information to determine past score position with high reliability, thus improving alignment accuracy, and the fact that the future position can be anticipated using an adaptively estimated tempo. Experiments using classical music and jazz databases demonstrate the validity of our approach.