Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Pablo Riera"'
Publikováno v:
ACI Avances en Ciencias e Ingenierías, Vol 7, Iss 2 (2015)
The Herbarium of Economic Botany of Ecuador QUSF of Universidad San Francisco de Quito (Ecuador), registered in the Index Herbariorum since 2001, holds over twenty thousand catalogued specimens of 2131 species of Magnoliophyta and Pteridophyta. One t
Externí odkaz:
https://doaj.org/article/8fbe04d8c6f54e5a9ce46e1e66cb5752
Autor:
Pablo Riera Freire
Publikováno v:
Biblioteca Digital de Teses e Dissertações do LNCCLaboratório Nacional de Computação CientíficaLNCC.
O câncer é uma doença originada a partir de mutações no genoma. Um dos principais tipos de mutação são as variações do número de cópias gênicas, que ocorre quando o número de cópias de uma determinada região genômica difere de dois.
Externí odkaz:
http://www.lncc.br/tdmc/tde_busca/arquivo.php?codArquivo=155
Autor:
Gabriel Pareyon, Carlos Almada, Carlos Mathias, Cecília Saraiva, Daniel Moreira, Hugo Carvalho, Liduino Pitombeira, Pauxy Gentil-Nunes, Bruno Mesz, Pablo Amster, Pablo Riera
Publikováno v:
MusMat|: Brazilian Journal of Music and Mathematics. 6:12-47
Publikováno v:
Behavior Research Methods. 54:712-728
Measuring human capabilities to synchronize in time, adapt to perturbations to timing sequences, or reproduce time intervals often requires experimental setups that allow recording response times with millisecond precision. Most setups present audito
Autor:
Lucas Samaruga, Pablo Riera
Publikováno v:
AudioMostly 2022.
Autor:
Lara Gauder, Leonardo Pepino, Pablo Riera, Silvina Brussino, Jazmín Vidal, Agustín Gravano, Luciana Ferrer
Publikováno v:
Computer Speech & Language. 80:101487
Publikováno v:
NIME 2022.
Transformers have revolutionized the world of deep learning, specially in the field of natural language processing. Recently, the Audio Spectrogram Transformer (AST) was proposed for audio classification, leading to state of the art results in severa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::999153c5b5dba02e9bf69d4136995adb
http://arxiv.org/abs/2110.06999
http://arxiv.org/abs/2110.06999
Publikováno v:
Interspeech 2021.
Publikováno v:
Interspeech 2021.
Emotion recognition datasets are relatively small, making the use of the more sophisticated deep learning approaches challenging. In this work, we propose a transfer learning method for speech emotion recognition where features extracted from pre-tra