16 papers
Where Flow Matching Leaks: Characterising Membership Signals Along the Interpolation Path
Thomas Sesmat, Gabriel Meseguer-Brocal, Geoffroy Peeters
Understanding memorization in generative models remains challenging, with implications for copyright and privacy. Beyond verbatim reproduction, models can encode subtler traces of…
S-SONDO: Self-Supervised Knowledge Distillation for General Audio Foundation Models
Mohammed Ali El Adlouni, Aurian Quelennec, Pierre Chouteau +2
General audio foundation models have recently achieved remarkable progress, enabling strong performance across diverse tasks. However, state-of-the-art models remain extremely larg…
S-PRESSO: Ultra Low Bitrate Sound Effect Compression With Diffusion Autoencoders And Offline Quantization
Zineb Lahrichi, Gaëtan Hadjeres, Gaël Richard +1
Neural audio compression models have recently achieved extreme compression rates, enabling efficient latent generative modeling. Conversely, latent generative models have been appl…
Twenty-Five Years of MIR Research: Achievements, Practices, Evaluations, and Future Challenges
Geoffroy Peeters, Zafar Rafii, Magdalena Fuentes +4
In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large par…
Controlling Contrastive Self-Supervised Learning with Knowledge-Driven Multiple Hypothesis: Application to Beat Tracking
Antonin Gagnere, Slim Essid, Geoffroy Peeters
Ambiguities in data and problem constraints can lead to diverse, equally plausible outcomes for a machine learning task. In beat and downbeat tracking, for instance, different list…
PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective
Alain Riou, Bernardo Torres, Ben Hayes +4
In this paper, we introduce PESTO, a self-supervised learning approach for single-pitch estimation using a Siamese architecture. Our model processes individual frames of a Variable…