Fabio Vesperini

TL;DR: Extensive evaluations carried out on three publicly available datasets are reported, showing how the CapsNet-based algorithm not only outperforms standard CNNs but also achieves the best results with respect to the state-of-the-art algorithms.

TL;DR: It is shown how DNN-based algorithm significantly outperforms the state-of-the-art approaches evaluated on the DIRHA dataset, providing an average localization error expressed in terms of Root Mean Square Error (RMSE), equal to 324 mm and 367 mm for the Simulated and the Real subsets.

TL;DR: A first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals is shown and is able to learn the differences between signals belonging to different classes of events.

TL;DR: This paper focuses on Voice Activity Detectors (VAD) for multi-room domestic scenarios based on deep neural network architectures and a comparative and extensive analysis is lead among four different neural networks (NN).

TL;DR: This paper presents and compares two algorithms based on artificial neural networks (ANNs) for sound event detection in real life audio and presents results obtained with two different neural architectures, namely multi-layer perceptrons (MLPs) and recurrent neural networks.