Statistical music modeling aimed at identification and alignment

Abstract. 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 corre-sponding music work. The model can be used to identify unknown recordings and to align them with the corresponding score. Experimental evaluation using a col-lection 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.