Research into music performance has always relied on and benefited from technical innovations. A major boost was given by the rise of MIDI technology allowing detailed recording of timing, dynamics and articulation in MIDI (Musical Instrument Digital Interface) instruments (generally keyboard). 

The increasing availability of audio analysis tools has opened possibilities to investigate performances from audio recordings paving the way for historical and contemporary databases of recordings to be analyzed with the constraint that different audio sources (e.g. different voices and instruments) cannot be separated. This reliance on technical innovations has however pigeon-holed performance analysis within two categories of academia: a computer-driven approach tackling large data sets using machine-learning techniques and a qualitative and small scale data analysis approach driven by performers and musicologists. 

This Network will bridge the gap across these disciplines, using digital transformation to fully integrate the approaches from the arts and sciences to provide meaningful research outcomes for theory and practice. Moreover, building on recent innovations, it will push forward the investigation of expressive communication in ensemble performance countering the existing focus on either solo performance or the ensemble.