Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Gauy, Marcelo Matheus"'
Autor:
Gauy, Marcelo Matheus, Koza, Natalia Hitomi, Morita, Ricardo Mikio, Stanzione, Gabriel Rocha, Junior, Arnaldo Candido, Berti, Larissa Cristina, Levin, Anna Sara Shafferman, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, Finger, Marcelo
We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification t
Externí odkaz:
http://arxiv.org/abs/2407.20989
Autor:
Gauy, Marcelo Matheus, Berti, Larissa Cristina, Cândido Jr, Arnaldo, Neto, Augusto Camargo, Goldman, Alfredo, Levin, Anna Sara Shafferman, Martins, Marcus, de Medeiros, Beatriz Raposo, Queiroz, Marcelo, Sabino, Ester Cerdeira, Svartman, Flaviane Romani Fernandes, Finger, Marcelo
Publikováno v:
Artificial Intellingence in Medicine Proceedings 2023, page 271-275
This work investigates Artificial Intelligence (AI) systems that detect respiratory insufficiency (RI) by analyzing speech audios, thus treating speech as a RI biomarker. Previous works collected RI data (P1) from COVID-19 patients during the first p
Externí odkaz:
http://arxiv.org/abs/2405.17569
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
An acoustic model, trained on a significant amount of unlabeled data, consists of a self-supervised learned speech representation useful for solving downstream tasks, perhaps after a fine-tuning of the model in the respective downstream task. In this
Externí odkaz:
http://arxiv.org/abs/2312.09265
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
Publikováno v:
First Workshop on Automatic Speech Recognition for Spontaneous and Prepared Speech Speech emotion recognition in Portuguese (SER 2022)
The goal of speech emotion recognition (SER) is to identify the emotional aspects of speech. The SER challenge for Brazilian Portuguese speech was proposed with short snippets of Portuguese which are classified as neutral, non-neutral female and non-
Externí odkaz:
http://arxiv.org/abs/2210.14716
Autor:
Gauy, Marcelo Matheus, Finger, Marcelo
Publikováno v:
SIMP\'OSIO BRASILEIRO DE TECNOLOGIA DA INFORMA\c{C}\~AO E DA LINGUAGEM HUMANA (STIL), 13. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computa\c{c}\~ao, 2021 . p. 143-152
This work explores speech as a biomarker and investigates the detection of respiratory insufficiency (RI) by analyzing speech samples. Previous work \cite{spira2021} constructed a dataset of respiratory insufficiency COVID-19 patient utterances and a
Externí odkaz:
http://arxiv.org/abs/2210.14085
Autor:
Gauy, Marcelo Matheus
Estudamos o problema de famílias intersectantes extremais em um subconjunto aleatório da família dos subconjuntos com exatamente k elementos de um conjunto dado. Obtivemos uma descrição quase completa da evolução do tamanho de tais famílias.
Autor:
Marzagão, David Kohan, Bonatto, Luciana Basualdo, Madeira, Tiago, Gauy, Marcelo Matheus, McBurney, Peter
Multi-agent consensus problems can often be seen as a sequence of autonomous and independent local choices between a finite set of decision options, with each local choice undertaken simultaneously, and with a shared goal of achieving a global consen
Externí odkaz:
http://arxiv.org/abs/2105.04666
Evolutionary Strategies (ES) are known to be an effective black-box optimization technique for deep neural networks when the true gradients cannot be computed, such as in Reinforcement Learning. We continue a recent line of research that uses surroga
Externí odkaz:
http://arxiv.org/abs/1910.05268
Autor:
Benzing, Frederik, Gauy, Marcelo Matheus, Mujika, Asier, Martinsson, Anders, Steger, Angelika
One of the central goals of Recurrent Neural Networks (RNNs) is to learn long-term dependencies in sequential data. Nevertheless, the most popular training method, Truncated Backpropagation through Time (TBPTT), categorically forbids learning depende
Externí odkaz:
http://arxiv.org/abs/1902.03993
Autor:
Einarsson, Hafsteinn, Gauy, Marcelo Matheus, Lengler, Johannes, Meier, Florian, Mujika, Asier, Steger, Angelika, Weissenberger, Felix
We study unbiased $(1+1)$ evolutionary algorithms on linear functions with an unknown number $n$ of bits with non-zero weight. Static algorithms achieve an optimal runtime of $O(n (\ln n)^{2+\epsilon})$, however, it remained unclear whether more dyna
Externí odkaz:
http://arxiv.org/abs/1808.05566