  Motivated by the state-of-art psychological research, we note that a piano
performance transcribed with existing Automatic Music Transcription (AMT)
methods cannot be successfully resynthesized without affecting the artistic
content of the performance. This is due to 1) the different mappings between
MIDI parameters used by different instruments, and 2) the fact that musicians
adapt their way of playing to the surrounding acoustic environment. To face
this issue, we propose a methodology to build acoustics-specific AMT systems
that are able to model the adaptations that musicians apply to convey their
interpretation. Specifically, we train models tailored for virtual instruments
in a modular architecture that takes as input an audio recording and the
relative aligned music score, and outputs the acoustics-specific velocities of
each note. We test different model shapes and show that the proposed
methodology generally outperforms the usual AMT pipeline which does not
consider specificities of the instrument and of the acoustic environment.
Interestingly, such a methodology is extensible in a straightforward way since
only slight efforts are required to train models for the inference of other
piano parameters, such as pedaling.
