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pro vyhledávání: '"Andrés Marafioti"'
Publikováno v:
IEEE Journal of Selected Topics in Signal Processing. 15:120-131
In this article, we introduce GACELA, a conditional generative adversarial network (cGAN) designed to restore missing audio data with durations ranging between hundreds of milliseconds and a few seconds, i.e., to perform long-gap audio inpainting. Wh
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 27:2362-2372
In this article, we study the ability of deep neural networks (DNNs) to restore missing audio content based on its context, i.e., inpaint audio gaps. We focus on a condition which has not received much attention yet: gaps in the range of tens of mill
Autor:
Martin S. Zinkernagel, Raphael Sznitman, Mathias Gallardo, Sebastian Wolf, Michel Hayoz, Andrés Marafioti, Pablo Marquez Neila
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872014
MICCAI (4)
MICCAI (4)
Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world. With such a large demand, the ability to organize surgical wards and operating rooms efficiently is critical to delivery this therapy in ro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0ddf7fcd773e7f656c5052687f0e631
In audio processing applications, phase retrieval (PR) is often performed from the magnitude of short-time Fourier transform (STFT) coefficients. Although PR performance has been observed to depend on the considered STFT parameters and audio data, th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a79e4894b29ca754a014b1c4ce5128f