6 citations · 16 across the 13 of their papers we have counts for
13 papers
Masked diffusion enables coherent beat tracking
Francesco Foscarin, Filip Korzeniowski, Richard Vogl
Current neural networks for beat tracking generate invalid outputs, such as consecutive downbeats and erratic tempo changes, even when these are not present in the training data. H…
Beat this! Accurate beat tracking without DBN postprocessing
Francesco Foscarin, Jan Schlüter, Gerhard Widmer
We propose a system for tracking beats and downbeats with two objectives: generality across a diverse music range, and high accuracy. We achieve generality by training on multiple…
Cluster and Separate: a GNN Approach to Voice and Staff Prediction for Score Engraving
Francesco Foscarin, Emmanouil Karystinaios, Eita Nakamura +1
This paper approaches the problem of separating the notes from a quantized symbolic music piece (e.g., a MIDI file) into multiple voices and staves. This is a fundamental part of t…
SMUG-Explain: A Framework for Symbolic Music Graph Explanations
Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer
In this work, we present Score MUsic Graph (SMUG)-Explain, a framework for generating and visualizing explanations of graph neural networks applied to arbitrary prediction tasks on…
Perception-Inspired Graph Convolution for Music Understanding Tasks
Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer
We propose a new graph convolutional block, called MusGConv, specifically designed for the efficient processing of musical score data and motivated by general perceptual principles…
8+8=4: Formalizing Time Units to Handle Symbolic Music Durations
Emmanouil Karystinaios, Francesco Foscarin, Florent Jacquemard +3
This paper focuses on the nominal durations of musical events (notes and rests) in a symbolic musical score, and on how to conveniently handle these in computer applications. We pr…