71 citations · 171 across the 19 of their papers we have counts for
23 papers
Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation
Elena V. Epure, Yashar Deldjoo, Bruno Sguerra +2
Music Recommender Systems (MRSs) have long relied on an information retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effe…
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…
The dynamics of discovery and the Heaps-Zipf relationship
Célestin Zimmerlin, Thomas Louail, Manuel Moussallam +1
When following a sequence - such as reading a text or tracking a user's activity - one can measure how the "dictionary" of distinct elements (types) grows with the number of observ…
"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…
Just Ask for Music (JAM): Multimodal and Personalized Natural Language Music Recommendation
Alessandro B. Melchiorre, Elena V. Epure, Shahed Masoudian +4
Natural language interfaces offer a compelling approach for music recommendation, enabling users to express complex preferences conversationally. While Large Language Models (LLMs)…
Modeling Musical Genre Trajectories through Pathlet Learning
Lilian Marey, Charlotte Laclau, Bruno Sguerra +2
The increasing availability of user data on music streaming platforms opens up new possibilities for analyzing music consumption. However, understanding the evolution of user prefe…
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…
Do Recommender Systems Promote Local Music? A Reproducibility Study Using Music Streaming Data
Kristina Matrosova, Lilian Marey, Guillaume Salha-Galvan +3
This paper examines the influence of recommender systems on local music representation, discussing prior findings from an empirical study on the LFM-2b public dataset. This prior s…
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…
Modeling Activity-Driven Music Listening with PACE
Lilian Marey, Bruno Sguerra, Manuel Moussallam
While the topic of listening context is widely studied in the literature of music recommender systems, the integration of regular user behavior is often omitted. In this paper, we…
Uplifting Interviews in Social Science with Individual Data Visualization: the case of Music Listening
Robin Cura, Amélie Beaumont, Jean-Samuel Beuscart +6
Collecting accurate and fine-grain information about the music people like, dislike and actually listen to has long been a challenge for sociologists. As millions of people now use…
Explainability in Music Recommender Systems
Darius Afchar, Alessandro B. Melchiorre, Markus Schedl +3
The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing thes…
Follow the guides: disentangling human and algorithmic curation in online music consumption
Quentin Villermet, Jérémie Poiroux, Manuel Moussallam +2
The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue. The quantitative state of the art often overlooks the existence of…
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin +1
Collaborative Metric Learning (CML) recently emerged as a powerful paradigm for recommendation based on implicit feedback collaborative filtering. However, standard CML methods lea…
Modeling the Music Genre Perception across Language-Bound Cultures
Elena V. Epure, Guillaume Salha, Manuel Moussallam +1
The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures. These variations cannot be modeled as mere…
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Guillaume Salha, Romain Hennequin, Jean-Baptiste Remy +2
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce FastGAE, a general f…
Improving Collaborative Metric Learning with Efficient Negative Sampling
Viet-Anh Tran, Romain Hennequin, Jimena Royo-Letelier +1
Distance metric learning based on triplet loss has been applied with success in a wide range of applications such as face recognition, image retrieval, speaker change detection and…
Disambiguating Music Artists at Scale with Audio Metric Learning
Jimena Royo-Letelier, Romain Hennequin, Viet-Anh Tran +1
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task. We explore the use of metric learning techniques to learn…
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
Rémi Delbouys, Romain Hennequin, Francesco Piccoli +2
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track. We reproduce the implementation of traditional feature engineering bas…
Audio Based Disambiguation Of Music Genre Tags
Romain Hennequin, Jimena Royo-Letelier, Manuel Moussallam
In this paper, we propose to infer music genre embeddings from audio datasets carrying semantic information about genres. We show that such embeddings can be used for disambiguatin…
Listening to features
Manuel Moussallam, Antoine Liutkus, Laurent Daudet
This work explores nonparametric methods which aim at synthesizing audio from low-dimensionnal acoustic features typically used in MIR frameworks. Several issues prevent this task…
Blind Denoising with Random Greedy Pursuits
Manuel Moussallam, Alexandre Gramfort, Laurent Daudet +1
Denoising methods require some assumptions about the signal of interest and the noise. While most denoising procedures require some knowledge about the noise level, which may be un…
Matching Pursuits with Random Sequential Subdictionaries
Manuel Moussallam, Laurent Daudet, Gaël Richard
Matching pursuits are a class of greedy algorithms commonly used in signal processing, for solving the sparse approximation problem. They rely on an atom selection step that requir…