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pro vyhledávání: '"Eric Guizzo"'
The modeling of human emotion expression in speech signals is an important, yet challenging task. The high resource demand of speech emotion recognition models, combined with the the general scarcity of emotion-labelled data are obstacles to the deve
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::111cdc4f2a9cc569b56401aa51c2afbe
Autor:
Eric Guizzo, Christian Marinoni, Marco Pennese, Xinlei Ren, Xiguang Zheng, Chen Zhang, Bruno Masiero, Aurelio Uncini, Danilo Comminiello
The L3DAS22 Challenge is aimed at encouraging the development of machine learning strategies for 3D speech enhancement and 3D sound localization and detection in office-like environments. This challenge improves and extends the tasks of the L3DAS21 e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3f414cadbeeb6f713124f8bc708ad729
Autor:
Giuseppe Nachira, Enrico Rocchi, Riccardo F. Gramaccioni, Marco Pennese, Eric Guizzo, Aurelio Uncini, Christian Marinoni, Danilo Comminiello, Sveva Pepe, Saeid Jamili, Claudia Medaglia, Ludovica Paglialunga, Edoardo Massaro, Leonardo Nucciarelli
Publikováno v:
MLSP
The L3DAS21 Challenge is aimed at encouraging and fostering collaborative research on machine learning for 3D audio signal processing, with particular focus on 3D speech enhancement (SE) and 3D sound localization and detection (SELD). Alongside with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec50eebcfec39e81ab09a30b41b64645
Publikováno v:
Neural Networks
We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for anot
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::78f5ab4342b5ed4028650875ab6c9173
https://pubmed.ncbi.nlm.nih.gov/34034071
https://pubmed.ncbi.nlm.nih.gov/34034071
Publikováno v:
ICASSP
Robustness against temporal variations is important for emotion recognition from speech audio, since emotion is expressed through complex spectral patterns that can exhibit significant local dilation and compression on the time axis depending on spea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::53b5d9ff343e60b50e61f645482c28b4
https://doi.org/10.1109/icassp40776.2020.9053727
https://doi.org/10.1109/icassp40776.2020.9053727
Autor:
Fermin Moscoso del Prado Martin, Giovanni Maffei, Francesco Barbieri, Federico Lucchesi, Tillman Weyde, Eric Guizzo
Publikováno v:
FG
We describe our system for empathic emotion recognition. It is based on deep learning on multiple modalities in a late fusion architecture. We describe the modules of our system and discuss the evaluation results. Our code is also available for the r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ab727d29fe98fb71eff014502b3e12e
https://doi.org/10.1109/fg.2019.8756532
https://doi.org/10.1109/fg.2019.8756532
Autor:
Eric Guizzo
Publikováno v:
IEEE Spectrum. 45:26-34
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a999d0c3fbe80417d6b2eb8133e54944
https://doi.org/10.1109/mspec.2008.4547508
https://doi.org/10.1109/mspec.2008.4547508