71 citations · 294 across the 49 of their papers we have counts for
57 papers
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…
Perceived Femininity in Singing Voice: Analysis and Prediction
Yuexuan Kong, Viet-Anh Tran, Romain Hennequin
This paper focuses on the often-overlooked aspect of perceived voice femininity in singing voices. While existing research has examined perceived voice femininity in speech, the sa…
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…
Exploring Large Action Sets with Hyperspherical Embeddings using von Mises-Fisher Sampling
Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +3
This paper introduces von Mises-Fisher exploration (vMF-exp), a scalable method for exploring large action sets in reinforcement learning problems where hyperspherical embedding ve…
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…
AI-Generated Song Detection via Lyrics Transcripts
Markus Frohmann, Elena V. Epure, Gabriel Meseguer-Brocal +2
The recent rise in capabilities of AI-based music generation tools has created an upheaval in the music industry, necessitating the creation of accurate methods to detect such AI-g…
A Fourier Explanation of AI-music Artifacts
Darius Afchar, Gabriel Meseguer-Brocal, Kamil Akesbi +1
The rapid rise of generative AI has transformed music creation, with millions of users engaging in AI-generated music. Despite its popularity, concerns regarding copyright infringe…
Uncertainty in Repeated Implicit Feedback as a Measure of Reliability
Bruno Sguerra, Viet-Anh Tran, Romain Hennequin +1
Recommender systems rely heavily on user feedback to learn effective user and item representations. Despite their widespread adoption, limited attention has been given to the uncer…
AI-Generated Music Detection and its Challenges
Darius Afchar, Gabriel Meseguer-Brocal, Romain Hennequin
In the face of a new era of generative models, the detection of artificially generated content has become a matter of utmost importance. In particular, the ability to create credib…
S-KEY: Self-supervised Learning of Major and Minor Keys from Audio
Yuexuan Kong, Gabriel Meseguer-Brocal, Vincent Lostanlen +2
STONE, the current method in self-supervised learning for tonality estimation in music signals, cannot distinguish relative keys, such as C major versus A minor. In this article, w…
Harnessing High-Level Song Descriptors towards Natural Language-Based Music Recommendation
Elena V. Epure, Gabriel Meseguer-Brocal, Darius Afchar +1
Recommender systems relying on Language Models (LMs) have gained popularity in assisting users to navigate large catalogs. LMs often exploit item high-level descriptors, i.e. categ…
Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation
Viet-Anh Tran, Guillaume Salha-Galvan, Bruno Sguerra +1
Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, mo…
From Real to Cloned Singer Identification
Dorian Desblancs, Gabriel Meseguer-Brocal, Romain Hennequin +1
Cloned voices of popular singers sound increasingly realistic and have gained popularity over the past few years. They however pose a threat to the industry due to personality righ…
STONE: Self-supervised Tonality Estimator
Yuexuan Kong, Vincent Lostanlen, Gabriel Meseguer-Brocal +3
Although deep neural networks can estimate the key of a musical piece, their supervision incurs a massive annotation effort. Against this shortcoming, we present STONE, the first s…
STraDa: A Singer Traits Dataset
Yuexuan Kong, Viet-Anh Tran, Romain Hennequin
There is a limited amount of large-scale public datasets that contain downloadable music audio files and rich lead singer metadata. To provide such a dataset to benefit research in…
Evaluating LLMs for Quotation Attribution in Literary Texts: A Case Study of LLaMa3
Gaspard Michel, Elena V. Epure, Romain Hennequin +1
Large Language Models (LLMs) have shown promising results in a variety of literary tasks, often using complex memorized details of narration and fictional characters. In this work,…
Improving Quotation Attribution with Fictional Character Embeddings
Gaspard Michel, Elena V. Epure, Romain Hennequin +1
Humans naturally attribute utterances of direct speech to their speaker in literary works. When attributing quotes, we process contextual information but also access mental represe…