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 ...
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.
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