Zobrazeno 1 - 10
of 435
pro vyhledávání: '"Alonso-Jiménez A"'
Publikováno v:
E3S Web of Conferences, Vol 349, p 01003 (2022)
Circular economy has, in the case of some raw materials, its own drawbacks. The principle states that reuse is basic to reduce primary production, thus in a healthy circular economy we should recycle as many elaborated raw materials as possible (in t
Externí odkaz:
https://doaj.org/article/879c080b315e4706adeffd975907e514
Autor:
Alonso-Jiménez, Pablo, Pepino, Leonardo, Batlle-Roca, Roser, Zinemanas, Pablo, Bogdanov, Dmitry, Serra, Xavier, Rocamora, Martín
We present PECMAE, an interpretable model for music audio classification based on prototype learning. Our model is based on a previous method, APNet, which jointly learns an autoencoder and a prototypical network. Instead, we propose to decouple both
Externí odkaz:
http://arxiv.org/abs/2402.09318
Music Information Retrieval (MIR) research is increasingly leveraging representation learning to obtain more compact, powerful music audio representations for various downstream MIR tasks. However, current representation evaluation methods are fragme
Externí odkaz:
http://arxiv.org/abs/2312.05994
In this work, we address music representation learning using convolution-free transformers. We build on top of existing spectrogram-based audio transformers such as AST and train our models on a supervised task using patchout training similar to PaSS
Externí odkaz:
http://arxiv.org/abs/2309.16418
Autor:
Alonso-Jiménez, Pablo, Favory, Xavier, Foroughmand, Hadrien, Bourdalas, Grigoris, Serra, Xavier, Lidy, Thomas, Bogdanov, Dmitry
In this work, we investigate an approach that relies on contrastive learning and music metadata as a weak source of supervision to train music representation models. Recent studies show that contrastive learning can be used with editorial metadata (e
Externí odkaz:
http://arxiv.org/abs/2304.12257
Publikováno v:
Pattern Recognition, Vol. 135, 2023
Prototype Generation (PG) methods are typically considered for improving the efficiency of the $k$-Nearest Neighbour ($k$NN) classifier when tackling high-size corpora. Such approaches aim at generating a reduced version of the corpus without decreas
Externí odkaz:
http://arxiv.org/abs/2207.10947
Autor:
Smeets, Nathalie, Gheldof, Alexander, Dequeker, Bart, Poleur, Margaux, Maldonado Slootjes, Sofia, Van Parijs, Vinciane, Deconinck, Nicolas, Dontaine, Pauline, Alonso-Jimenez, Alicia, De Bleecker, Jan, De Ridder, Willem, Herdewyn, Sarah, Paquay, Stéphanie, Vanlander, Arnaud, De Waele, Liesbeth, Peirens, Geertrui, Beysen, Diane, Claeys, Kristl G., Dubuisson, Nicolas, Hansen, Isabelle, Remiche, Gauthier, Seneca, Sara, Bissay, Véronique, Régal, Luc
Publikováno v:
In Pediatric Neurology September 2024 158:57-65
Publikováno v:
Medicina y Seguridad del Trabajo, Vol 69, Iss 271, Pp 77-99 (2023)
Resumen En términos económicos y preventivos, la vacunación se ha demostrado como la medida más eficaz y rentable para prevenir enfermedades infecciosas inmunoprevenibles, tanto a nivel individual como comunitario. La gestión de los riesgos biol
Externí odkaz:
https://doaj.org/article/d5e8973e31554d49a97a766480602cd3
Publikováno v:
Applied Sciences, Vol 14, Iss 17, p 7826 (2024)
Metallum Fire-Resistant paint, denoted as MFR henceforth, represents a cutting-edge insulating material with dual functionality as a fireproof solution, presenting substantial advantages in the realm of construction applications. This exposition deri
Externí odkaz:
https://doaj.org/article/d2ad0a964504429893ea689d1c03e097
Essentia is a reference open-source C++/Python library for audio and music analysis. In this work, we present a set of algorithms that employ TensorFlow in Essentia, allow predictions with pre-trained deep learning models, and are designed to offer f
Externí odkaz:
http://arxiv.org/abs/2003.07393