Symbolic music representations encode musical content at varying levels of completeness, from performance-oriented formats such as MIDI to fully notated scores such as MusicXML. Yet the impact of this representational richness on deep learning models remains poorly understood. While tokenisation has been extensively st...
Andrea Poltronieri, Matteo Spanio, Simone Chieppa et al.· 1 citation
MambaVoice, a lightweight audiovisual framework that leverages a hybrid Mamba--Transformer architecture for targeted singing voice separation, highlights the effectiveness of hybrid SSM--attention architectures for scalable, efficient audiovisual source separation, suggesting they are well-suited as lightweight compone...
Adithi Shankar, G. Krishnan, G. Haro et al.· 0 citations
PANEL, an open-source, self-hosted platform for generative models studies that reports per-question summaries, across-condition significance tests, pairwise win rates and Bradley--Terry scores, and supports power analysis from pilot data, is presented.
Matteo Spanio, Andrea Poltronieri, Martín Rocamora· 0 citations
MusGU+ is introduced, a musician-centered evaluation framework organized around three dimensions: Adaptability, Usability, and Controllability that supports informed selection and practical adoption of generative systems by musicians.
Laura Ibáñez-Martínez, Roser Batlle-Roca, Xavier Serra et al.· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
