computational musicology

Taking a Byzantine music dataset with 400 music pieces from three music sources, we perform computational classifications into echos using three content features: pitches, intervals, and Byzantine signs (called voiced units, which represent melodic movement)

We introduce a novel perspective on set-class analysis combining the DFT magnitudes with the music visualisation technique of wavescapes.

In this study, we employ a computational model that formalizes music-theoretic conceptualizations of tonal space, and use it to infer the most likely interval distributions for pieces in a large corpus of music, represented as so-called ‘bags of tonal pitch classes’.

Exploring the potential of digitization and computational models for Brazil’s musical cultural heritage