Search Results for author:
Found 10 papers, 8 papers with code
Modéliser la perception des genres musicaux à travers différentes cultures à partir de ressources linguistiques (Modeling the Music Genre Perception across Language-Bound Cultures )
Nous résumons nos travaux de recherche, présentés à la conférence EMNLP 2020 et portant sur la modélisation de la perception des genres musicaux à travers différentes cultures, à partir de représentations sémantiques spécifiques à différentes langues.
Explainability in Music Recommender Systems
In this article, we discuss how explainability can be addressed in the context of MRSs.
Collaborative Filtering
Explainable artificial intelligence
+3
Follow the guides: disentangling human and algorithmic curation in online music consumption
The role of recommendation systems in the diversity of content consumption on platforms is a much-debated issue.
Hierarchical Latent Relation Modeling for Collaborative Metric Learning
Existing extensions of CML also either ignore the heterogeneity of user-item relations, i. e. that a user can simultaneously like very different items, or the latent item-item relations, i. e. that a user's preference for an item depends, not only on its intrinsic characteristics, but also on items they previously interacted with.
Modeling the Music Genre Perception across Language-Bound Cultures
The music genre perception expressed through human annotations of artists or albums varies significantly across language-bound cultures.
Cultural Vocal Bursts Intensity Prediction
Information Retrieval
+2
FastGAE: Scalable Graph Autoencoders with Stochastic Subgraph Decoding
Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues.
Spleeter: A Fast And State-of-the Art Music Source Separation Tool With Pre-trained Models
We present and release a new tool for music source separation with pre-trained models called Spleeter. Spleeter was designed with ease of use, separation performance and speed in mind.
Ranked #19 on
Music Source Separation
on MUSDB18
(using extra training data)
Improving Collaborative Metric Learning with Efficient Negative Sampling
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 recently recommendation with the CML model.
Disambiguating Music Artists at Scale with Audio Metric Learning
We address the problem of disambiguating large scale catalogs through the definition of an unknown artist clustering task.
Music Mood Detection Based On Audio And Lyrics With Deep Neural Net
We consider the task of multimodal music mood prediction based on the audio signal and the lyrics of a track.
