Several MIR applications require fine-grained note alignments between MIDI performances
and their musical scores for training and evaluation. However, large and high-quality …

Music Information Retrieval (MIR) has seen a recent surge in deep learning-based
approaches, which often involve encoding symbolic music (ie, music represented in terms of …

This paper targets the perceptual task of separating the different interacting voices, ie,
monophonic melodic streams, in a polyphonic musical piece. We target symbolic music …

Partitura is a lightweight Python package for handling symbolic musical information. It
provides easy access to features commonly used in music information retrieval tasks, like …

We present the Batik-plays-Mozart Corpus, a piano performance dataset combining
professional Mozart piano sonata performances with expert-labelled scores at a note …

Aligning versions of the same source material has been a persistent challenge in the field of
digital libraries for musicology, and a barrier to progress. The growing number of publicly …

Recent advances in data-driven expressive performance rendering have enabled automatic
models to reproduce the characteristics and the variability of human performances of …

Generative models of expressive piano performance are usually assessed by comparing
their predictions to a reference human performance. A generative algorithm is taken to be …

We propose a new graph convolutional block, called MusGConv, specifically designed for
the efficient processing of musical score data and motivated by general perceptual …

This dissertation on data-driven music theory is centered around curatorial practices
concerning the creation, publication, and evaluation of large, expert-annotated symbolic …