Match!
Emanuele Principi
Marche Polytechnic University
77Publications
12H-index
394Citations
Publications 78
Newest
#1Fabio Vesperini (Marche Polytechnic University)H-Index: 7
#2Luca Romeo (Marche Polytechnic University)H-Index: 7
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 5 authors...
In this paper, we propose an algorithm for snoring sounds detection based on convolutional recurrent neural networks (CRNN). The log Mel energy spectrum of the audio signal is extracted from overnight recordings and is used as input to the CRNN with the aim to detect the precise onset and offset time of the sound events. The dataset used in the experiments is highly imbalanced toward the non-snore class. A data augmentation technique is introduced, that consists in generating new snore examples ...
Source
#1Paolo Vecchiotti (Marche Polytechnic University)H-Index: 4
#2Giovanni Pepe (Marche Polytechnic University)
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 4 authors...
Abstract The task of Speaker LOCalization (SLOC) has been the focus of numerous works in the research field, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD) algorithm. Nevertheless, this perfect working condition is not satisfied in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, finall...
Source
#1Fabio Vesperini (Marche Polytechnic University)H-Index: 7
#2Leonardo Gabrielli (Marche Polytechnic University)H-Index: 7
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 4 authors...
Artificial sound event detection (SED) aims to mimic the human ability to perceive and understand what is happening in the surroundings. Nowadays, deep learning offers valuable techniques for this goal, such as convolutional neural networks (CNNs). The capsule neural network (CapsNet) architecture has been recently introduced in the image processing field with the intent to overcome some of the known limitations of CNNs, specifically regarding the scarce robustness to affine transformations (i.e...
6 CitationsSource
#1Marco FagianiH-Index: 6
#2Roberto BonfigliH-Index: 5
Last.Luigi MandoliniH-Index: 1
view all 5 authors...
Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all poss...
2 CitationsSource
#1Emanuele Principi (Marche Polytechnic University)H-Index: 12
#2Damiano RossettiH-Index: 2
Last.Francesco Piazza (Marche Polytechnic University)H-Index: 24
view all 4 authors...
Fault diagnosis of electric motors is a fundamental task for production line testing, and it is usually performed by experienced human operators. In the recent years, several methods have been proposed in the literature for detecting faults automatically. Deep neural networks have been successfully employed for this task, but, up to the authors ʼ knowledge, they have never been used in an unsupervised scenario. This paper proposes an unsupervised method for diagnosing faults of electric mo...
Source
#1Marco Severini (Marche Polytechnic University)H-Index: 6
#2Daniele Ferretti (Marche Polytechnic University)H-Index: 2
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 4 authors...
Cry detection is an important facility in both residential and public environments, which can answer to different needs of both private and professional users. In this paper, we investigate the problem of cry detection in professional environments, such as Neonatal Intensive Care Units (NICUs). The aim of our work is to propose a cry detection method based on deep neural networks (DNNs) and also to evaluate whether a properly designed synthetic dataset can replace on-field acquired data for trai...
1 CitationsSource
Source
#1Paolo Vecchiotti (Marche Polytechnic University)H-Index: 4
#2Emanuele Principi (Marche Polytechnic University)H-Index: 12
Last.Francesco Piazza (Marche Polytechnic University)H-Index: 24
view all 4 authors...
Detecting the presence of speakers and suitably localize them in indoor environments undoubtedly represent two important tasks in the speech processing community. Several algorithms have been proposed for Voice Activity Detection (VAD) and Speaker LOCalization (SLOC) so far, while their accomplishment by means of a joint integrated model has not received much attention. In particular, no studies focused on cooperative exploitation of VAD and SLOC information by means of machine learning have bee...
2 CitationsSource
#1Daniele Ferretti (Marche Polytechnic University)H-Index: 2
#2Marco Severini (Marche Polytechnic University)H-Index: 6
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 5 authors...
The amount of time an infant cries in a day helps the medical staff in the evaluation of his/her health conditions. Extracting this information requires a cry detection algorithm able to operate in environments with challenging acoustic conditions, since multiple noise sources, such as interferent cries, medical equipments, and persons may be present. This paper proposes an algorithm for detecting infant cries in such environments. The proposed solution is a multiple stage detection algorithm: t...
Source
#1Fabio Vesperini (Marche Polytechnic University)H-Index: 7
#2Diego Droghini (Marche Polytechnic University)H-Index: 3
Last.Stefano Squartini (Marche Polytechnic University)H-Index: 18
view all 5 authors...
In this paper, we propose a system for rare sound event detection using a hierarchical and multi-scaled approach based on Convolutional Neural Networks (CNN). The task consists on detection of event onsets from artificially generated mixtures. Spectral features are extracted from frames of the acoustic signals, then a first event detection stage operates as binary classifier at frame-rate and it proposes to the second stage contiguous blocks of frames which are assumed to contain a sound event. ...
Source
12345678