  This Thesis discusses the development of technologies for the automatic
resynthesis of music recordings using digital synthesizers. First, the main
issue is identified in the understanding of how Music Information Processing
(MIP) methods can take into consideration the influence of the acoustic context
on the music performance. For this, a novel conceptual and mathematical
framework named "Music Interpretation Analysis" (MIA) is presented. In the
proposed framework, a distinction is made between the "performance" - the
physical action of playing - and the "interpretation" - the action that the
performer wishes to achieve. Second, the Thesis describes further works aiming
at the democratization of music production tools via automatic resynthesis: 1)
it elaborates software and file formats for historical music archiving and
multimodal machine-learning datasets; 2) it explores and extends MIP
technologies; 3) it presents the mathematical foundations of the MIA framework
and shows preliminary evaluations to demonstrate the effectiveness of the
approach
