5 papers
Graph Kernel Neural Networks
Luca Cosmo, Giorgia Minello, Alessandro Bicciato +4
The convolution operator at the core of many modern neural architectures can effectively be seen as performing a dot product between an input matrix and a filter. While this is rea…
FolAI: Synchronized Foley Sound Generation with Semantic and Temporal Alignment
Riccardo Fosco Gramaccioni, Christian Marinoni, Emilian Postolache +4
Traditional sound design workflows rely on manual alignment of audio events to visual cues, as in Foley sound design, where everyday actions like footsteps or object interactions a…
STAGE: Stemmed Accompaniment Generation through Prefix-Based Conditioning
Giorgio Strano, Chiara Ballanti, Donato Crisostomi +3
Recent advances in generative models have made it possible to create high-quality, coherent music, with some systems delivering production-level output. Yet, most existing models f…
Generating Graphs via Spectral Diffusion
Giorgia Minello, Alessandro Bicciato, Luca Rossi +2
In this paper, we present GGSD, a novel graph generative model based on 1) the spectral decomposition of the graph Laplacian matrix and 2) a diffusion process. Specifically, we pro…
COCOLA: Coherence-Oriented Contrastive Learning of Musical Audio Representations
Ruben Ciranni, Giorgio Mariani, Michele Mancusi +4
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coher…