A comparative study of tonal acoustic features for a symbolic level music-to-score alignment

In this paper we review the acoustic features used for music-to-score alignment and study their influence on the performance in a challenging alignment task, where the audio data is polyphonic and may contain percussion. Furthermore, as we aim at using “real world” scores, we follow an approach which does exploit the rhythm information (considered unreliable) and test its robustness to score errors. We use a unified framework to handle different state-of-the-art features, and propose a simple way to exploit either a model of the feature values, or an audio synthesis of a musical score, in an audio-to-score alignment system. We confirm that chroma vectors drawn from representations using a logarithmic frequency scale are the most efficient features, and lead to a good precision, even with a simple alignment strategy. Robustness tests also show that the relative performance of the features do not depend on possible musical score degradations.