Eric Guizzo
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View article: Learning Speech Emotion Representations in the Quaternion Domain
Learning Speech Emotion Representations in the Quaternion Domain Open
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 general scarcity of emotion-labelled data are obstacles to t…
L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment Open
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 th…
Learning Speech Emotion Representations in the Quaternion Domain Open
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 …
L3DAS22 Challenge: Learning 3D Audio Sources in a Real Office Environment Open
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 th…
L3DAS21 Challenge: Machine Learning for 3D Audio Signal Processing Open
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). Alon…
L3DAS21 Challenge Open
L3DAS21: MACHINE LEARNING FOR 3D AUDIO SIGNAL PROCESSING IEEE MLSP Data Challenge 2021 SCOPE OF THE CHALLENGE The L3DAS21 Challenge for the IEEE MLSP 2021 aims at encouraging and fostering research on machine learning for 3D audio signal p…
L3DAS21 Challenge Open
L3DAS21: MACHINE LEARNING FOR 3D AUDIO SIGNAL PROCESSING\n\nIEEE MLSP Data Challenge 2021\n\n \n\nSCOPE OF THE CHALLENGE\n\nThe L3DAS21 Challenge for the IEEE MLSP 2021 aims at encouraging and fostering research on machine learning for 3D …
L3DAS21 challenge: machine learning for 3D audio signal processing Open
The L3DAS21 Challenge11www.13das.com/mlsp2021 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 an…
Blissful Ignorance: Anti-Transfer Learning for Task Invariance. Open
We introduce the novel concept of anti-transfer learning for neural networks. While standard transfer learning assumes that the representations learned in one task will be useful for another task, anti-transfer learning avoids learning rep…
Anti-Transfer Learning for Task Invariance in Convolutional Neural\n Networks for Speech Processing Open
We introduce the novel concept of anti-transfer learning for speech\nprocessing with convolutional neural networks. While transfer learning assumes\nthat the learning process for a target task will benefit from re-using\nrepresentations le…
Multi-Time-Scale Convolution for Emotion Recognition from Speech Audio Signals Open
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 depen…
A Neural Network Based Framework for Archetypical Sound Synthesis Open
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…