activity
20222024
most citedEx2Vec: Characterizing Users and Items from the Mere Exposure Effect

10 citations · 14 across the 8 of their papers we have counts for

collaborators

8 papers

cs.SD2024

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…

cs.SD2024

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…

cs.SD2024

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…

cs.CL2024

Distinguishing Fictional Voices: a Study of Authorship Verification Models for Quotation Attribution

Gaspard Michel, Elena V. Epure, Romain Hennequin +1

Recent approaches to automatically detect the speaker of an utterance of direct speech often disregard general information about characters in favor of local information found in t…

cs.IR202310 cited

Ex2Vec: Characterizing Users and Items from the Mere Exposure Effect

Bruno Sguerra, Viet-Anh Tran, Romain Hennequin

The traditional recommendation framework seeks to connect user and content, by finding the best match possible based on users past interaction. However, a good content recommendati…

cs.IR20231 cited

On the Consistency of Average Embeddings for Item Recommendation

Walid Bendada, Guillaume Salha-Galvan, Romain Hennequin +2

A prevalent practice in recommender systems consists in averaging item embeddings to represent users or higher-level concepts in the same embedding space. This paper investigates t…