In any piano performance, expressiveness is paramount for effectively conveying the intent of the performer, and one of the most significant aspects of expressiveness is the loudness at the individual key or note level. However, accurately detecting note-level loudness poses a considerable technical challenge due to the polyphonic nature of piano performances, wherein multiple notes are played simultaneously, as well as the similarity of harmonic elements. MIDI velocity is crucial for indicating loudness in piano notes. This study conducted experiments for estimating a note-level MIDI velocity expanding the DiffRoll model: the Diffusion Model for piano performance transcription. By adopting double conditioning—audio and score information—and implementing noise removal as a post-processing, our findings highlight the model’s potential in estimating note level MIDI velocity.

This research was carried out under the project Musical AI - PID2019- 111403GBI00/AEI/10.13039/501100011033, funded by the Spanish Ministerio de Ciencia e Innovación and the Agencia Estatal de Investigación.

This work has been accepted at the CMMR2023, the 16th International Symposium on Computer Music Multidisciplinary Research, at Tokyo, Japan. November 13-17, 2023.