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pro vyhledávání: '"Guizzo, Eric"'
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:
http://arxiv.org/abs/2204.02385
Autor:
Guizzo, Eric, Marinoni, Christian, Pennese, Marco, Ren, Xinlei, Zheng, Xiguang, Zhang, Chen, Masiero, Bruno, Uncini, Aurelio, Comminiello, Danilo
Publikováno v:
2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022, pp. 9186-9190
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:
http://arxiv.org/abs/2202.10372
Autor:
Guizzo, Eric, Gramaccioni, Riccardo F., Jamili, Saeid, Marinoni, Christian, Massaro, Edoardo, Medaglia, Claudia, Nachira, Giuseppe, Nucciarelli, Leonardo, Paglialunga, Ludovica, Pennese, Marco, Pepe, Sveva, Rocchi, Enrico, Uncini, Aurelio, Comminiello, Danilo
Publikováno v:
2021 IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), 2021, pp. 1-6
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:
http://arxiv.org/abs/2104.05499
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:
http://arxiv.org/abs/2006.06494
Robustness against temporal variations is important for emotion recognition from speech audio, since emotion is ex-pressed through complex spectral patterns that can exhibit significant local dilation and compression on the time axis depending on spe
Externí odkaz:
http://arxiv.org/abs/2003.03375
Autor:
Guizzo, Eric, Novello, Alberto
This paper describes a preliminary approach to algorithmically reproduce the archetypical structure adopted by humans to classify sounds. In particular, we propose an approach to predict the human perceived chaos/order level in a sound and synthesize
Externí odkaz:
http://arxiv.org/abs/2003.03160
Publikováno v:
In Neural Networks October 2021 142:238-251
Akademický článek
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Autor:
Guizzo, Erico Marui
Thesis (S.M. in Science Writing)--Massachusetts Institute of Technology, Dept. of Humanities, Program in Writing and Humanistic Studies, 2003.
Includes bibliographical references (leaves [70]-77).
In 1948, Claude Shannon, a young engineer a
Includes bibliographical references (leaves [70]-77).
In 1948, Claude Shannon, a young engineer a
Externí odkaz:
http://hdl.handle.net/1721.1/39429