From the 1 of 13 linked papers with an AI index.
13 papers
Detection of AI-generated stems within hybrid human-AI music
François Rigaud, Gabriel Meseguer-Brocal, Benjamin Martin +1
The paper investigates how to detect AI‑generated stems in hybrid human‑AI music tracks, proposing a parallel architecture that combines source‑separation‑based energy estimation w…
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…
Learning Linearity in Audio Consistency Autoencoders via Implicit Regularization
Bernardo Torres, Manuel Moussallam, Gabriel Meseguer-Brocal
Audio autoencoders learn useful, compressed audio representations, but their non-linear latent spaces prevent intuitive algebraic manipulation such as mixing or scaling. We introdu…
"Beyond the past": Leveraging Audio and Human Memory for Sequential Music Recommendation
Viet-Anh Tran, Bruno Sguerra, Gabriel Meseguer-Brocal +2
On music streaming services, listening sessions are often composed of a balance of familiar and new tracks. Recently, sequential recommender systems have adopted cognitive-informed…
Multi-Class-Token Transformer for Multitask Self-supervised Music Information Retrieval
Yuexuan Kong, Vincent Lostanlen, Romain Hennequin +2
Contrastive learning and equivariant learning are effective methods for self-supervised learning (SSL) for audio content analysis. Yet, their application to music information retri…
Emergent musical properties of a transformer under contrastive self-supervised learning
Yuexuan Kong, Gabriel Meseguer-Brocal, Vincent Lostanlen +2
In music information retrieval (MIR), contrastive self-supervised learning for general-purpose representation models is effective for global tasks such as automatic tagging. Howeve…