Musical notes are often modeled as a discrete sequence of points on a frequency spectrum with possibly different interval sizes such as just-intonation. Computational descriptions abstracting the pitch content in audio music recordings have used this model, with reasonable success in several information retrieval tasks. In this paper, we argue that this model restricts a deeper understanding of the pitch content. First, we discuss a statistical model of musical notes which widens the scope of the current one and opens up possibilities to create new ways to describe the pitch content. Then we present a computational approach that partially aligns the audio recording with its music score in a hierarchical manner first at metrical cyclelevel and then at note-level, to describe the pitch content using this model. It is evaluated extrinsically in a classification test using a public dataset and the result is shown to be significantly better compared to a state-of-the-art approach. Further, similar results obtained on a more challenging dataset which we have put together, reinforces that our approach outperforms the other.

This research was partly funded by the European Research Council under the European Union’s Seventh Framework Program, as part of the CompMusic project (ERC grant agreement 267583).

Comunicació presentada a: 13th Sound and Music Computing Conference (SMC 2016), celebrat a Hamburg (Alemanya), del 31 d'agost a 3 de setembre de 2016.