no code implementations • 24 Nov 2023 • Francesco Paissan, Elisabetta Farella
Contrastive Language-Audio Pretraining (CLAP) became of crucial importance in the field of audio and speech processing.
no code implementations • ICCV 2023 • Alberto Ancilotto, Francesco Paissan, Elisabetta Farella
The recent interest in the edge-to-cloud continuum paradigm has emphasized the need for simple and scalable architectures to deliver optimal performance on computationally constrained devices.
no code implementations • 8 Jun 2022 • Irene Martín-Morató, Francesco Paissan, Alberto Ancilotto, Toni Heittola, Annamaria Mesaros, Elisabetta Farella, Alessio Brutti, Tuomas Virtanen
The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46. 5 K parameters, and 29. 23 million multiply-and-accumulate operations (MMACs).
1 code implementation • Developmental Cognitive Neuroscience special édition 2022 • Velu Prabhakar Kumaravel, Eugenio Parise, Elisabetta Farella, Marco Buiatti
We propose Newborn EEG Artifact Removal (NEAR), a pipeline for EEG artifact removal designed explicitly for human newborns.
no code implementations • 1 Oct 2021 • Francesco Paissan, Alberto Ancilotto, Elisabetta Farella
In the Internet of Things era, where we see many interconnected and heterogeneous mobile and fixed smart devices, distributing the intelligence from the cloud to the edge has become a necessity.
no code implementations • 2 Feb 2021 • Francesco Paissan, Massimo Gottardi, Elisabetta Farella
Therefore, domain-specific pipelines are usually delivered in order to exploit the full potential of these cameras.
no code implementations • 12 Jan 2021 • Gianmarco Cerutti, Renzo Andri, Lukas Cavigelli, Michele Magno, Elisabetta Farella, Luca Benini
This BNN reaches a 77. 9% accuracy, just 7% lower than the full-precision version, with 58 kB (7. 2 times less) for the weights and 262 kB (2. 4 times less) memory in total.
no code implementations • 29 Jan 2020 • Gianmarco Cerutti, Rahul Prasad, Alessio Brutti, Elisabetta Farella
This paper addresses the application of sound event detection at the edge, by optimizing deep learning techniques on resource-constrained embedded platforms for the IoT.