activity
20182025
most citedSinging voice separation: a study on training data

38 citations · 153 across the 11 of their papers we have counts for

collaborators

20 papers

cs.IR2024

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…

cs.LG2022

New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction

Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2

Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…

cs.HC202213 cited

Discovery Dynamics: Leveraging Repeated Exposure for User and Music Characterization

Bruno Sguerra, Viet-Anh Tran, Romain Hennequin

Repetition in music consumption is a common phenomenon. It is notably more frequent when compared to the consumption of other media, such as books and movies. In this paper, we sho…

cs.LG202119 cited

Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders

Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus +2

On an artist's profile page, music streaming services frequently recommend a ranked list of "similar artists" that fans also liked. However, implementing such a feature is challeng…

cs.IR2021

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…

cs.SD20216 cited

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…