71 citations · 294 across the 49 of their papers we have counts for
8 papers · 1 filter
Evaluating perceptual transparency of lossy music audio compression with Opus codec
Benjamin Martin, Romain Hennequin
Although the open-source Opus audio codec is widely used for digital audio streaming, its perceptual transparency threshold for high-bitrate music compression remains insufficientl…
A Stem-Agnostic Approach to Hybrid AI Music Detection
Richa Namballa, François Rigaud, Romain Hennequin
The inclusion of generative audio in the music production process has led to an increase in hybrid music tracks that blend authentic human performances with AI-generated stems, cha…
CoJEPA: Combining Contrastive Learning and JEPA for Global-Local Music Representations
Gabriel Meseguer-Brocal, Yuexuan Kong, Romain Hennequin
Joint-Embedding Predictive Architecture (JEPA) has shown strong performance in learning rich representations through self-supervised prediction in latent space. However, it typical…
Improved Robustness in AI-Generated Music Detection
Emile Dugelay, Thomas Barand, Aurélien Laouar +3
AI music generators leave predictable spectral artifacts determined by their architecture. Existing detectors exploit these artifacts with near-perfect accuracy on raw generated tr…
Detection of AI-generated stems within hybrid human-AI music
François Rigaud, Gabriel Meseguer-Brocal, Benjamin Martin +1
This paper presents, to the best of our knowledge, the first study on detecting human-AI hybrid music tracks created by mixing human-produced and AI-generated stems. Building on re…
Finding the noise: Zero-shot AI Music Detection
Darius Afchar, Romain Hennequin
We present a novel method for AI-generated music detection in scenarios where the models that generated the input samples are unknown to the detector (e.g., from a newly released s…
GraphLit: Learning Text-Enriched Dynamic Character Network Representations for Literary Study
Gaspard Michel, Elena V. Epure, Romain Hennequin +2
Methods to represent literary texts as graphs or sequences of graphs mainly focus on representing character interactions, and often overlook another crucial aspect: the textual con…
Beyond Musical Descriptors: Extracting Preference-Bearing Intent in Music Queries
Marion Baranes, Romain Hennequin, Elena V. Epure
Although annotated music descriptor datasets for user queries are increasingly common, few consider the user's intent behind these descriptors, which is essential for effectively m…