Statistical Music Modeling Aimed at Identification and Alignment

This paper describes a methodology for the statistical modeling of music works. Starting from either the representation of the symbolic score or the audio recording of a performance, a hidden Markov model is built to represent the corresponding music work. The model can be used to identify unknown recordings and to align them with the corresponding score. Experimental evaluation using a collection of classical music recordings showed that this approach is effective in terms of both identification and alignment. The methodology can be exploited as the core component for a set of tools aimed at accessing and actively listening to a music collection.