71 citations · 294 across the 49 of their papers we have counts for
23 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…
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…
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…
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…
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…
Detecting music deepfakes is easy but actually hard
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. The ability to create credible minute-long…
An Experimental Comparison Of Multi-view Self-supervised Methods For Music Tagging
Gabriel Meseguer-Brocal, Dorian Desblancs, Romain Hennequin
Self-supervised learning has emerged as a powerful way to pre-train generalizable machine learning models on large amounts of unlabeled data. It is particularly compelling in the m…
Learning Unsupervised Hierarchies of Audio Concepts
Darius Afchar, Romain Hennequin, Vincent Guigue
Music signals are difficult to interpret from their low-level features, perhaps even more than images: e.g. highlighting part of a spectrogram or an image is often insufficient to…
Singing Language Identification using a Deep Phonotactic Approach
Lenny Renault, Andrea Vaglio, Romain Hennequin
Extensive works have tackled Language Identification (LID) in the speech domain, however their application to the singing voice trails and performances on Singing Language Identifi…
Leveraging Knowledge Bases And Parallel Annotations For Music Genre Translation
Elena V. Epure, Anis Khlif, Romain Hennequin
Prevalent efforts have been put in automatically inferring genres of musical items. Yet, the propose solutions often rely on simplifications and fail to address the diversity and s…
Singing voice separation: a study on training data
Laure Prétet, Romain Hennequin, Jimena Royo-Letelier +1
In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial fact…