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…
Predicting Music Hierarchies with a Graph-Based Neural Decoder
Francesco Foscarin, Daniel Harasim, Gerhard Widmer
This paper describes a data-driven framework to parse musical sequences into dependency trees, which are hierarchical structures used in music cognition research and music analysis…
The ACCompanion: Combining Reactivity, Robustness, and Musical Expressivity in an Automatic Piano Accompanist
Carlos Cancino-Chacón, Silvan Peter, Patricia Hu +5
This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a…
Musical Voice Separation as Link Prediction: Modeling a Musical Perception Task as a Multi-Trajectory Tracking Problem
Emmanouil Karystinaios, Francesco Foscarin, Gerhard Widmer
This paper targets the perceptual task of separating the different interacting voices, i.e., monophonic melodic streams, in a polyphonic musical piece. We target symbolic music, wh…
Concept-Based Techniques for "Musicologist-friendly" Explanations in a Deep Music Classifier
Francesco Foscarin, Katharina Hoedt, Verena Praher +2
Current approaches for explaining deep learning systems applied to musical data provide results in a low-level feature space, e.g., by highlighting potentially relevant time-freque…
The match file format: Encoding Alignments between Scores and Performances
Francesco Foscarin, Emmanouil Karystinaios, Silvan David Peter +3
This paper presents the specifications of match: a file format that extends a MIDI human performance with note-, beat-, and downbeat-level alignments to a corresponding musical sco…
Partitura: A Python Package for Symbolic Music Processing
Carlos Cancino-Chacón, Silvan David Peter, Emmanouil Karystinaios +3
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 no…
PKSpell: Data-Driven Pitch Spelling and Key Signature Estimation
Francesco Foscarin, Nicolas Audebert, Raphaël Fournier-S'Niehotta
We present PKSpell: a data-driven approach for the joint estimation of pitch spelling and key signatures from MIDI files. Both elements are fundamental for the production of a full…