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Francesco PIAZZA

Pubblicazioni

Francesco PIAZZA

 

418 pubblicazioni classificate nel seguente modo:

Nr. doc. Classificazioni
274 4 Contributo in Atti di Convegno (Proceeding)
79 1 Contributo su Rivista
64 2 Contributo in Volume
1 3 Libro
Anno Risorsa
2019 Unsupervised electric motor fault detection by using deep autoencoders
IEEE/CAA JOURNAL OF AUTOMATICA SINICA
Autore/i: Principi, Emanuele; Rossetti, Damiano; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: 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 motors by using a novelty detection approach based on deep autoencoders. In the proposed method, vibration signals are acquired by using accelerometers and processed to extract Log-Mel coefficients as features. Autoencoders are trained by using normal data only, i.e., data that do not contain faults. Three different autoencoders architectures have been evaluated: the multi-layer perceptron ( MLP ) autoencoder, the convolutional neural network autoencoder, and the recurrent autoencoder composed of long short-term memory ( LSTM ) units. The experiments have been conducted by using a dataset created by the authors, and the proposed approaches have been compared to the one-class support vector machine ( OC-SVM ) algorithm. The performance has been evaluated in terms area under curve ( AUC ) of the receiver operating characteristic curve, and the results showed that all the autoencoder-based approaches outperform the OC-SVM algorithm. Moreover, the MLP autoencoder is the most performing architecture, achieving an AUC equal to 99.11 %.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/264608 Collegamento a IRIS

2019 A Swept-Sine Pulse Compression Procedure for an Effective Measurement of Intermodulation Distortion
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
Autore/i: Burrascano, Pietro; Laureti, Stefano; Ricci, Marco; Terenzi, Alessandro; Cecchi, Stefania; Spinsante, Susanna; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/265697 Collegamento a IRIS

2018 Identification of nonlinear audio devices exploiting multiple-variance method and perfect sequences
144th Audio Engineering Society Convention 2018
Autore/i: Orcioni, Simone; Carini, Alberto; Cecchi, Stefania; Terenzi, Alessandro; Piazza, Francesco
Editore: Audio Engineering Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259284 Collegamento a IRIS

2018 Identification of Volterra Models of Tube Audio Devices using Multiple-Variance Method
AES
Autore/i: Orcioni, Simone; Terenzi, Alessandro; Cecchi, Stefania; Piazza, Francesco; Carini, Alberto
Classificazione: 1 Contributo su Rivista
Abstract: The multiple-variance method is a cross-correlation method that exploits input signals with different powers for the identification of a nonlinear system by means of the Volterra series. It overcomes the problem of the locality of the solution of traditional nonlinear identification methods, based on mean square error minimization or cross-correlation, that well approximate the system only for inputs that have approximately the same power of the identification signal. The multiple-variance method permits to improve the performance of models of systems that have inputs with high dynamic, like audio amplifiers. This method is used, for the first time, to identify three different tube amplifiers. The method is applied to a novel reduced Volterra model that allows to overcome the problem of the very large number of coefficients required by the Volterra series by choosing only a proper subset of elements from each kernel. Eventually, the multiple-variance methodology is applied to different real audio tube devices demonstrating the effectiveness of the proposed approach in terms of system identification and computational complexity.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261341 Collegamento a IRIS

2018 Few-Shot Siamese Neural Networks Employing Audio Features for Human-Fall Detection
Proceedings of PRAI2018
Autore/i: Droghini, Diego; Vesperini, Fabio; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Nowadays, the detection of human fall is a problem recognized by the entire scientific community. Methods that have good performance use human falls samples in the train set, while methods that do not use it, can only work well under certain conditions. Since examples of human falls are very difficult to retrieve, there is a strong need to develop systems that can work well event with few or no data to be used for their training phase. In this article, we show a first study on few-shot learning Siamese Neural Network applied to human falls detection by using audio signals. This method has been compared with algorithms based on SVM and OCSVM, all evaluated starting from the same conditions. The proposed approach is able to learn the differences between signals belonging to different classes of events. In classification phase, using only one human fall signal as a template, it achieves about 80% of F1 -Measure related to the human fall class, while the SVM based method gets around 69%, when it is trained in the same data knowledge conditions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262772 Collegamento a IRIS

2018 Computational Intelligence Based Demand Response Management in a Microgrid
IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Autore/i: Herath, Pramod; Fusco, Vito; Navarro, María; Venayagamoorthy, Ganesh K.; Squartini, Stefano; Piazza, Francesco; Manuel Corchado, Juan
Classificazione: 1 Contributo su Rivista
Abstract: A demand response management (DRM) system is proposed here, in which a service provider determines a mutual optimal solution for the utility and the customers in a microgrid setting. Such a system may find use with a service provider interacting with the respective customers and utilities under the existence of some DRM agreements. The service provider is an entity which acts at different levels of the electrical grid and carry out the optimization. The lowest level controls one ‘neighborhood’ while higher levels of service providers control other lower level service providers. A microgrid consisting of a smart neighborhood of twelve customers was used as experimental case study and an advanced metering infrastructure (AMI) was implemented. Based on the formulation of an optimization problem which exploits price-responsive demand flexibility and the AMI infrastructure, a win-win-win strategy is presented. The interior point method was used to solve the objective function and the application of particle swarm optimization and artificial immune systems for demand response was explored. Results for a range of typical scenarios were presented to demonstrate the effectiveness of the proposed demand-response management framework.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/260765 Collegamento a IRIS

2018 End-to-end learning for physics-based acoustic modeling
IEEE Transactions on Emerging Topics in Computational Intelligence
Autore/i: Gabrielli, L.; Tomassetti, S.; Zinato, C.; Piazza, F.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266324 Collegamento a IRIS

2018 A novel measurement procedure for wiener/hammerstein classification of nonlinear audio systems
144th Audio Engineering Society Convention 2018
Autore/i: Primavera, Andrea; Gasparini, Michele; Cecchi, Stefania; Hariya, Wataru; Murai, Shogo; Oishi, Koji; Piazza, Francesco
Editore: Audio Engineering Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261708 Collegamento a IRIS

2018 An End-To-End unsupervised approach employing convolutional neural network autoencoders for human fall detection
Smart Innovation, Systems and Technologies
Autore/i: Droghini, Diego; Ferretti, Daniele; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Editore: Springer Science and Business Media Deutschland GmbH
Classificazione: 2 Contributo in Volume
Abstract: In the past few years, several works describing systems for the prompt detection of falls have been presented in literature. Many of these systems address the problem of fall detection by using some handcrafted features extracted from the input signals. In the meantime interest in the use of feature learning and deep architectures has been increasing, thus reducing the required engineering effort and the need for prior knowledge. A fall detection method based on a Deep Convolutional Neural Network Autoencoder is presented in this work. This method is trained as a novelty detector through the end-to-end strategy. The classifier distinguishes normal sound events generated by common indoor human activity (i.e. footsteps and speech) and music background from novelty sound events produced by human falls. The performance of the algorithm has been assessed on a corpus of fall events created by the authors. Moreover a comparison was made with two different state-of-art algorithms both based on a One Class Support Vector Machine. The results showed an improvement on performance of about 11% on average.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262784 Collegamento a IRIS

2017 Energy management with support of PV partial shading modelling in Micro Grid environments
ENERGIES
Autore/i: Severini, Marco; Principi, Emanuele; Fagiani, Marco; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: Although photovoltaic power plants are suitable local energy sources in Micro Grid environments, when large plants are involved, partial shading and inaccurate modelling of the plant can affect both the design of the Micro Grid as well as the energy management process that allows for lowering the overall Micro Grid demand towards the main grid. To investigate the issue, a Photovoltaic Plant simulation model, based on a real life power plant, and an energy management system, based on a real life Micro Grid environment, have been integrated to evaluate the performance of a Micro Grid under partial shading conditions. Using a baseline energy production model as a reference, the energy demand of the Micro Grid has been computed in sunny and partial shading conditions. The experiments reveal that an estimation based on a simplified PV model can exceed by 65% the actual production. With regards to Micro Grid design, on sunny days, the expected costs, based on a simplified PV model, can be 5.5% lower than the cost based on the double inverter model. In single cloud scenarios, the underrating can reach 28.3%. With regard to the management process, if the energy yield is estimated by means of a simplified PV model, the actual cost can be from 17.1% to 21.5% higher than the theoretical cost expected at design time.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249946 Collegamento a IRIS

2017 User-aided Footprint Extraction for Appliance Modelling in Non-Intrusive Load Monitoring
SSCI 2016, Proceedings on
Autore/i: Bonfigli, Roberto; Principi, Emanuele; Squartini, Stefano; Fagiani, Marco; Severini, Marco; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the area of Non-Intrusive Load Monitoring (NILM), many approaches need a supervised procedure of appliance modelling, in order to provide the informations about the appliances to the disaggregation algorithm and to obtain the disaggregated consumptions related to each one of them. In many approaches, the appliance modelling relies on the consumption footprint, which is a typical working cycle of the appliance. Since the NILM system has only the aggregated power consumption available, the recorded footprint might be corrupted by other appliances, which can not be turned off during this period, i.e., the fridge and freezer in the household. Furthermore, the user needs a facilitated procedure, in order to obtain a clean footprint from the aggregated power signal in real scenario. Therefore, a user-aided footprint extraction procedure is needed. In this work, this procedure is defined as a NILM problem with two sources, i.e., the desired appliance and the fridge-freezer combination. One of the resulting disaggregated profiles of the algorithm corresponds to the extracted footprint. Then, this is used for the appliance modelling stage to create te corresponding Hidden Markov Model (HMM), suitable for the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The effectiveness of the footprint extraction procedure is evaluated through the confidence of the disaggregation output of a real problem, using a span of 30 days data taken from two different datasets (AMPds, ECO). The experiments are conducted using the HMM from the extracted footprint, compared to the con- fidence of the same problem using the HMM from the true footprint, as appliance level consumption. The results show that the performance are comparable, with the worst relative F1 loss of 3.83%, demonstrating the effectiveness of the footprint extraction procedure.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240732 Collegamento a IRIS

2017 Localizing speakers in multiple rooms by using Deep Neural Networks
COMPUTER SPEECH AND LANGUAGE
Autore/i: Vesperini, Fabio; Vecchiotti, Paolo; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: In the field of human speech capturing systems, a fundamental role is played by the source localization algorithms. In this paper a Speaker Localization algorithm (SLOC) based on Deep Neural Networks (DNN) is evaluated and compared with state-of-the art approaches. The speaker position in the room under analysis is directly determined by the DNN, leading the proposed algorithm to be fully data-driven. Two different neural network architectures are investigated: the Multi Layer Perceptron (MLP) and Convolutional Neural Networks (CNN). GCC-PHAT (Generalized Cross Correlation-PHAse Transform) Patterns, computed from the audio signals captured by the microphone are used as input features for the DNN. In particular, a multi-room case study is dealt with, where the acoustic scene of each room is influenced by sounds emitted in the other rooms. The algorithm is tested by means of the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room. In detail, the focus goes to speaker localization task in two distinct neighboring rooms. As term of comparison, two algorithms proposed in literature for the addressed applicative context are evaluated, the Crosspower Spectrum Phase Speaker Localization (CSP-SLOC) and the Steered Response Power using the Phase Transform speaker localization (SRP-SLOC). Besides providing an extensive analysis of the proposed method, the article shows 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, respectively, for the Simulated and the Real subsets.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252452 Collegamento a IRIS

2017 Acoustic novelty detection with adversarial autoencoders
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Principi, Emanuele; Vesperini, Fabio; Squartini, Stefano; Piazza, Francesco
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Novelty detection is the task of recognising events the differ from a model of normality. This paper proposes an acoustic novelty detector based on neural networks trained with an adversarial training strategy. The proposed approach is composed of a feature extraction stage that calculates Log-Mel spectral features from the input signal. Then, an autoencoder network, trained on a corpus of 'normal' acoustic signals, is employed to detect whether a segment contains an abnormal event or not. A novelty is detected if the Euclidean distance between the input and the output of the autoencoder exceeds a certain threshold. The innovative contribution of the proposed approach resides in the training procedure of the autoencoder network: instead of using the conventional training procedure that minimises only the Minimum Mean Squared Error loss function, here we adopt an adversarial strategy, where a discriminator network is trained to distinguish between the output of the autoencoder and data sampled from the training corpus. The autoencoder, then, is trained also by using the binary cross-entropy loss calculated at the output of the discriminator network. The performance of the algorithm has been assessed on a corpus derived from the PASCAL CHiME dataset. The results showed that the proposed approach provides a relative performance improvement equal to 0.26% compared to the standard autoencoder. The significance of the improvement has been evaluated with a one-tailed z-test and resulted significant with p < 0.001. The presented approach thus showed promising results on this task and it could be extended as a general training strategy for autoencoders if confirmed by additional experiments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252454 Collegamento a IRIS

2017 Non-intrusive load monitoring by using active and reactive power in additive Factorial Hidden Markov Models
APPLIED ENERGY
Autore/i: Bonfigli, Roberto; Principi, Emanuele; Fagiani, Marco; Severini, Marco; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: Non-intrusive load monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the joint use of active and reactive power in the Additive Factorial Hidden Markov Models framework. In particular, in the proposed approach, the appliance model is represented by a bivariate Hidden Markov Model whose emitted symbols are the joint active-reactive power signals. The disaggregation is performed by means of an alternative formulation of the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm for dealing with the bivariate HMM models. The proposed solution has been compared to the original AFAMAP algorithm based on the active power only and to the seminal approach proposed by Hart (1992), based on finite state machine appliance models and which employs both the active and reactive power. Hart's algorithm has been improved for handling the occurrence of multiple solutions by means of a Maximum A Posteriori technique (MAP). The experiments have been conducted on the AMPds dataset in noised and denoised conditions and the performance evaluated by using the F1-Measure and the normalized disaggregation metrics. In terms of F1-Measure, the results showed that the proposed approach outperforms AFAMAP, Hart's algorithm, and Hart's with MAP respectively by +14.9%, +21.8%, and +2.5% in the 6 appliances denoised case study. In the 6 appliances noised case study, the relative performance improvement is +25.5%, +51.1%, and +6.7%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252450 Collegamento a IRIS

2017 Denoising autoencoders for Non-Intrusive Load Monitoring: Improvements and comparative evaluation
ENERGY AND BUILDINGS
Autore/i: Bonfigli, Roberto; Felicetti, Andrea; Principi, Emanuele; Fagiani, Marco; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: Non-Intrusive Load Monitoring (NILM) is the task of determining the appliances individual contributions to the aggregate power consumption by using a set of electrical parameters measured at a single metering point. NILM allows to provide detailed consumption information to the users, that induces them to modify their habits towards a wiser use of the electrical energy. This paper proposes a NILM algorithm based on the Deep Neural Networks. In particular, the NILM task is treated as a noise reduction problem addressed by using denoising autoencoder (dAE) architecture, i.e., a neural network trained to reconstruct a signal from its noisy version. This architecture has been initially proposed by Kelly and Knottenbelt (2015), and here is extended and improved by conducting a detailed study on the topology of the network, and by intelligently recombining the disaggregated output with a median filter. An additional contribution of this paper is an exhaustive comparative evaluation conducted with respect to one of the reference work in the field of Hidden Markov Models (HMM) for NILM, i.e., the Additive Factorial Approximate Maximum a Posteriori (AFAMAP) algorithm. The experiments have been conducted on the AMPds, UK-DALE, and REDD datasets in seen and unseen scenarios both in presence and in absence of noise. In order to be able to evaluate AFAMAP in presence of noise, an HMM model representing the noise contribution has been introduced. The results showed that the dAE approach outperforms the AFAMAP algorithm both in seen and unseen condition, and that it exhibits a significant robustness in presence of noise.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252451 Collegamento a IRIS

2017 Convolutional Neural Networks with 3-D Kernels for Voice Activity Detection in a Multiroom Environment
Multidisciplinary Approaches to Neural Computing
Autore/i: Vecchiotti, P.; Vesperini, F.; Principi, E.; Squartini, S.; Piazza, F
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: This paper focuses on employing Convolutional Neural Networks (CNN) with 3-D kernels for Voice Activity Detectors in multi-room domestic scenarios (mVAD). This technology is compared with the Multi Layer Perceptron (MLP) and interesting advancements are observed with respect to previous works of the authors. In order to approximate real- life scenarios, the DIRHA dataset is exploited. It has been recorded in a home environment by means of several microphones arranged in vari- ous rooms. Our study is composed by a multi-stage analysis focusing on the selection of the network size and the input microphones in relation with their number and position. Results are evaluated in terms of Speech Activity Detection error rate (SAD). The CNN-mVAD outperforms the other method with a significant solidity in terms of performance statis- tics, achieving in the best overall case a SAD equal to 7.0%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/241540 Collegamento a IRIS

2017 A Combined One-Class SVM and Template-Matching Approach for User-Aided Human Fall Detection by Means of Floor Acoustic Features
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Autore/i: Droghini, Diego; Ferretti, Daniele; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: The primary cause of injury-related death for the elders is represented by falls. The scientific community devoted them particular attention, since injuries can be limited by an early detection of the event. The solution proposed in this paper is based on a combined One-Class SVM (OCSVM) and template-matching classifier that discriminate human falls from nonfalls in a semisupervised framework. Acoustic signals are captured by means of a Floor Acoustic Sensor; then Mel-Frequency Cepstral Coefficients and Gaussian Mean Supervectors (GMSs) are extracted for the fall/nonfall discrimination. Here we propose a single-sensor two-stage user-aided approach: in the first stage, the OCSVM detects abnormal acoustic events. In the second, the template-matching classifier produces the final decision exploiting a set of template GMSs related to the events marked as false positives by the user. The performance of the algorithm has been evaluated on a corpus containing human falls and nonfall sounds. Compared to the OCSVM only approach, the proposed algorithm improves the performance by 10.14% in clean conditions and 4.84% in noisy conditions. Compared to Popescu and Mahnot (2009) the performance improvement is 19.96% in clean conditions and 8.08% in noisy conditions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249952 Collegamento a IRIS

2017 Human Fall Detection by Using an Innovative Floor Acoustic Sensor
Multidisciplinary Approaches to Neural Computing
Autore/i: Droghini, D.; Principi, E.; Squartini, S.; Piazza, F
Editore: Springer, Cham
Classificazione: 2 Contributo in Volume
Abstract: Supporting people in their homes is an important issue both for ethical and practical reasons. Indeed, in the recent years, the scientific community devoted particular attention to detecting human falls, since the first cause of death for elderly people is due to the consequences of a fall. In this paper, we propose a human fall classification system based on an innovative floor acoustic sensor able to capture the acoustic waves transmitted through the floor. The algorithm employed is able to discriminate human falls from non falls and it is based on Mel-Frequency Cepstral Coefficients and a two class Support Vector Machine. The dataset employed for performance evaluation is composed by falls of a human mimicking doll, everyday objects and everyday noises. The obtained results show that the proposed solution is suitable for human fall detection in realistic scenarios, allowing to guarantee a 0% miss probability at very low false positive rates.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/241539 Collegamento a IRIS

2017 Multichannel acoustic echo cancellation exploiting effective fundamental frequency estimation
SPEECH COMMUNICATION
Autore/i: Romoli, Laura; Cecchi, Stefania; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/241190 Collegamento a IRIS

2016 SW framework for simulation and evaluation of partial shading effects in configurable PV systems
2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC)
Autore/i: Severini, Marco; Andrea, Scorrano; Squartini, Stefano; Fagiani, Marco; Piazza, Francesco
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This SW framework is proposed as a design tool, which simulates the energy production of the system, while considering the characteristics of a photovoltaic (PV) power plant. The framework is made available upon request to the scientific community. The model takes into account not only the specification of the panels, the topology of the array and the conditions of the weather, but also the irradiation and the shading patterns, as well as the maximum power point tracking (MPPT) algorithm. The proposed tool allows to evaluate the performance of PV power plants being designed, while taking into account not only the design parameters but also the environmental characteristics, including the weather or terrain. However, differently from other tools, it also allows to compare the performance of MPPT algorithms, thus supporting the design of the PV power plant. Our experiments reveal that, in a plant of 4160 panels and about 6691 square meters, with a peak power of 998.5 kWp, dividing the PV modules among 13 inverter (320 modules each) and supporting the MPPT procedure, the yield loss can be lowered by about 13% with respect to a centralized PV plant with standard MPPT approaches. Over a year, the estimated increase of the income is over 3200 C.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238340 Collegamento a IRIS

2016 A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids
ENERGIES
Autore/i: Fagiani, Marco; Squartini, Stefano; Gabrielli, Leonardo; Severini, Marco; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90% in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238339 Collegamento a IRIS

2016 Smart AMI based demand-response management in a micro-grid environment
Clemson University Power Systems Conference, PSC 2016
Autore/i: Fusco, Vito; Venayagamoorthy, Ganesh K.; Squartini, Stefano; Piazza, Francesco
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Reliable operation of the electrical grid requires balancing between generation and energy demand at any time instant. Increasing penetration of intermittent sources of alternative generation compromises reliability and introduces significant price volatility. As a solution, demand response strategies have been studied to provide the necessary demand-side flexibility for utility to absorb some volatility. In this paper, a demand-response management (DRM) system is proposed, where a service provider finds a mutual optimal solution for the utility and the customers in a microgrid setting. This could be used by a service provider interacting with the respective customers and utility under the existence of some DRM agreements. In this study, a micro-grid consisting of a smart neighbourhood of twelve customers is taken as experimental case study and an advanced metering infrastructure (AMI) is implemented. Based on the formulation of an optimization problem which exploits price-responsive demand flexibility and the AMI infrastructure, a win-win-win strategy is presented. By shaping load patterns according to market pricing, the proposed method led to higher cost savings for the flexible customers and the utility, with consistent profit margins achieved by the service provider. Results for a range of typical scenarios are presented to demonstrate the effectiveness of the proposed demand-response management framework.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238341 Collegamento a IRIS

2016 Acoustic cues from the floor: A new approach for fall classification
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Principi, Emanuele; Droghini, Diego; Squartini, Stefano; Olivetti, Paolo; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context, the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. The audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors and Support Vector Machines to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of a human mimicking doll and of everyday objects. The results showed that the floor sensor significantly improves the performance respect to an aerial microphone: in particular, the F1-Measure is 6.50% higher in clean conditions and 8.76% higher in mismatched noisy conditions. The proposed approach, thus, has a considerable advantage over aerial solutions since it is able to achieve higher fall classification performance using a simpler algorithmic pipeline and hardware setup.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236004 Collegamento a IRIS

2016 Deep neural networks for Multi-Room Voice Activity Detection: Advancements and comparative evaluation
Neural Networks (IJCNN), 2016 International Joint Conference on
Autore/i: Vesperini, Fabio; Vecchiotti, Paolo; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper focuses on Voice Activity Detectors (VAD) for multi-room domestic scenarios based on deep neural network architectures. Interesting advancements are observed with respect to a previous work. A comparative and extensive analysis is lead among four different neural networks (NN). In particular, we exploit Deep Belief Network (DBN), Multi-Layer Perceptron (MLP), Bidirectional Long Short-Term Memory recurrent neural network (BLSTM) and Convolutional Neural Network (CNN). The latter has recently encountered a large success in the computational audio processing field and it has been successfully employed in our task. Two home recorded datasets are used in order to approximate real-life scenarios. They contain audio files from several microphones arranged in various rooms, from whom six features are extracted and used as input for the deep neural classifiers. The output stage has been redesigned compared to the previous author's contribution, in order to take advantage of the networks discriminative ability. Our study is composed by a multi-stage analysis focusing on the selection of the features, the network size and the input microphones. Results are evaluated in terms of Speech Activity Detection error rate (SAD). As result, a best SAD equal to 5.8% and 2.6% is reached respectively in the two considered datasets. In addiction, a significant solidity in terms of microphone positioning is observed in the case of CNN.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/239799 Collegamento a IRIS

2016 Improving the performance of the AFAMAP algorithm for Non-Intrusive Load Monitoring
Evolutionary Computation (CEC), 2016 IEEE Congress on
Autore/i: Bonfigli, Roberto; Severini, Marco; Squartini, Stefano; Fagiani, Marco; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Among the many electrical load disaggregation methods, often referred to as Non-Intrusive Load Monitoring techniques, the Additive Factorial Approximate MAP (AFAMAP) algorithm has shown outstanding capabilities and, therefore, it is nowadays regarded as a reference model. In order to achieve more accurate disaggregation results, and to satisfy real life environment requirements, further improvements in the algorithm are needed. In this work, the AFAMAP algorithm has been extended, by means of a differential forward model, thus complementing the existing differential backward model. Furthermore, an aggregated data examination method has been employed, aimed to the detection of inadmissible working state combinations of appliances, as well as the constraints setting based on the reactive power disaggregation feedback. The new approach has been evaluated by means of a subset, spanning over 6 months, of the Almanac of Minutely Power dataset (AMPds). On purpose, a real life environment, accounting 6 appliances, has been modelled and the carried out experiments revealed a improvement up to 18% with respect to the baseline AFAMAP.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240731 Collegamento a IRIS

2016 Exploiting temporal features and pressure data for automatic leakage detection in smart water grids
Evolutionary Computation (CEC), 2016 IEEE Congress on
Autore/i: Fagiani, Marco; Squartini, Stefano; Bonfigli, Roberto; Severini, Marco; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper, the unsupervised approach recently proposed by the authors for automatic leakage detection in smart water grids is extended. First of all, the EPANET tool is adopted in order to simulate more realistic leakages. Also, with respect to the original work, an additional time resolution, of 30 minutes, is included, based on the water dataset of the Almanac of Minutely Power Dataset (AMPds). New experiments are performed, as well, to evaluate the results of the application of both temporal features and pressure data. The pressure data is obtained by means of the EPANEt tool, whereas the leakages are induced at run-time for a more realistic behaviour. Two alternative sets of temporal features are evaluated by combining them with the features extracted from both flow and pressure data. Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and One-Class Support Vector Machine (OC-SVM) are used to characterize the normal data behaviour, under a comparative perspective. A feature selection strategy is adopted in computer simulations and the resulting performance indices are evaluated in terms of Area Under Curve (AUC). The obtained results show that the introduction of the temporal information produces a slight performance improvement for both flow and pressure data, but, most importantly, the combination of flow and pressure features allows a significant improvement of leakage detection for both GMM and HMM at every resolution, up to 88% of AUC.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240730 Collegamento a IRIS

2016 A neural network based algorithm for speaker localization in a multi-room environment
Machine Learning for Signal Processing (MLSP), 2016 IEEE 26th International Workshop on
Autore/i: Vesperini, Fabio; Vecchiotti, Paolo; Principi, Emanuele; Squartini, Stefano; Piazza, Francesco
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: A Speaker Localization algorithm based on Neural Networks for multi-room domestic scenarios is proposed in this paper. The approach is fully data-driven and employs a Neural Network fed by GCC-PHAT (Generalized Cross Correlation Phase Transform) Patterns, calculated by means of the microphone signals, to determine the speaker position in the room under analysis. In particular, we deal with a multi-room case study, in which the acoustic scene of each room is influenced by sounds emitted in the other rooms. The algorithm is tested against the home recorded DIRHA dataset, characterized by multiple wall and ceiling microphone signals for each room. In particular, we focused on the speaker localization problem in two distinct neighbouring rooms. We assumed the presence of an Oracle multi-room Voice Activity Detector (VAD) in our experiments. A three-stage optimization procedure has been adopted to find the best network configuration and GCC-PHAT Patterns combination. Moreover, an algorithm based on Time Difference of Arrival (TDOA), recently proposed in literature for the addressed applicative context, has been considered as term of comparison. As result, the proposed algorithm outperforms the reference one, providing an average localization error, expressed in terms of RMSE, equal to 525 mm against 1465 mm. Concluding, we also assessed the algorithm performance when a real VAD, recently proposed by some of the authors, is used. Even though a degradation of localization capability is registered (an average RMSE equal to 770 mm), still a remarkable improvement with respect to the state of the art performance is obtained.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/239800 Collegamento a IRIS

2016 Investigation on audio algorithms architecture for stereo portable devices
AES
Autore/i: Cecchi, Stefania; Virgulti, Marco; Primavera, Andrea; Piazza,Francesco; Bettarelli, Ferruccio; Li, Junfeng
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233954 Collegamento a IRIS

2016 ELM Based Algorithms for Acoustic Template Matching in Home Automation Scenarios: Advancements and Performance Analysis
Recent Advances in Nonlinear Speech Processing
Autore/i: Della Porta, G.; Principi, E.; Ferroni, G.; Squartini, S.; Hussain, A.; Piazza, F.
Classificazione: 2 Contributo in Volume
Abstract: Speech and sound recognition in home automation scenarios has been gaining an increasing interest in the last decade. One interesting approach addressed in the literature is based on the template matching paradigm, which is characterized by ease of implementation and independence on large datasets for system training. Moving from a recent contribution of some of the authors, where an Extreme Learn-ing Machine algorithm was proposed and evaluated, a wider performance analysis in diverse operating conditions is provided here, together with some relevant improvements. These are allowed by the employment of supervector features as input, for the first time used with ELMs, up to the authors’ knowledge. As already verified in other application contexts and with different learning systems, this ensures a more robust characterization of the speech segment to be classified, also in presence of mismatch between training and testing data. The accomplished computer simulations confirm the effectiveness of the approach, with F1-Measure performance up to 99% in the multicondition case, and a computational time reduction factor close to 4, with respect to the SVM counterpart.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230583 Collegamento a IRIS

2016 A comparison of optimization methods for compression driver design
140th Audio Engineering Society International Convention 2016, AES 2016
Autore/i: Gasparini, Michele; Capucci, Emiliano; Cecchi, Stefania; Toppi, Romolo; Piazza, Francesco
Editore: Audio Engineering Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240527 Collegamento a IRIS

2016 Automatic localization of a virtual sound image generated by a stereophonic configuration
140th Audio Engineering Society International Convention 2016, AES 2016
Autore/i: Romoli, Laura; Cecchi, Stefania; Bettarelli, Ferruccio; Piazza, Francesco
Editore: Audio Engineering Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240525 Collegamento a IRIS

2016 Combining evolution strategies and neural network procedures for compression driver design
Neural Networks (IJCNN), 2016 International Joint Conference on
Autore/i: Gasparini, Michele; Vesperini, Fabio; Cecchi, Stefania; Squartini, Stefano; Piazza, Francesco; Toppi, Romolo
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Compression driver design involves the study of complex mathematical models characterized by a great number of variables, implying high computational cost and long design time. Therefore, an optimization procedure is required to enhance the design procedure, especially from the parameters point of view. In this paper, a combined approach based both on evolution strategy procedure and neural network model is presented. Taking into consideration several tests on a real compression driver, the proposed method is capable to enhance the design procedure from the point of view of obtained frequency response and of the computational performance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/239797 Collegamento a IRIS

2016 An innovative structure for the approximation of stereo reverberation effect using mixed FIR/IIR filters
140th Audio Engineering Society International Convention 2016, AES 2016
Autore/i: Primavera, Andrea; Cecchi, Stefania; Romoli, Laura; Gasparini, Michele; Piazza, Francesco
Editore: Audio Engineering Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/240526 Collegamento a IRIS

2016 Multichannel Double-Talk Detector based on Fundamental Frequency Estimation
IEEE SIGNAL PROCESSING LETTERS
Autore/i: Cecchi, Stefania; Romoli, Laura; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233953 Collegamento a IRIS

2015 Intelligent Acoustic Interfaces with Multisensor Acquisition for Immersive Reproduction
IEEE TRANSACTIONS ON MULTIMEDIA
Autore/i: Comminiello, Danilo; Cecchi, Stefania; Scarpiniti, Michele; Gasparini, Michele; Romoli, Laura; Piazza, Francesco; Uncini, Aurelio
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/229025 Collegamento a IRIS

2015 An Interactive Optimization Procedure for Stereophonic Acoustic Echo Cancellation Systems
Proc. International Joint Conference on Neural Network
Autore/i: Romoli, Laura; Cecchi, Stefania; Piazza, Francesco; Comminiello, Danilo; Scarpiniti, Michele; Uncini, Aurelio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233961 Collegamento a IRIS

2015 A Novelty Detection approach to identify the occurrence of leakage in Smart Gas and Water Grids
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Fagiani, M.; Squartini, S.; Severini, M.; Piazza, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper, a novelty detection algorithm for the identification of leakages in smart water/gas grid contexts is proposed. It is based on two separate stages: the first deals with the creation of the statistical leakage-free model, whereas the second evaluates the eventual occurrence of leakage on the basis of the model likelihood. Up to the authors' knowledge, this approach has never been used in the application scenario of interest. A set of several features are extracted from the Almanac of Minutely Power Dataset, and a suboptimal selection is executed to determinate the best combination. The abnormal event (leakage) is induced by manipulating the consumption in the test set. A total of 10 background models are created, by employing both Gaussian Mixture Models (GMMs) and Hidden Markov Models (HMMs) under a comparative perspective, and each of them is adopted to detect 10 leakages, with random duration, length and starting time. Finally, the performance are evaluated in terms of Area Under Curve (AUC) of the Receiver Operating Characteristic (ROC). Obtained results are more than encouraging: the best average AUCs of 85.60% and 87.97% are achieved with HMM, at 1 minute resolution, for natural gas and water, respectively. Specifically, considering true detection rates (TDRs) of 100%, the natural gas exhibits an overall false detection rate (FDR) of 17.11%, and the water achieves an overall FDR of 13.79%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230584 Collegamento a IRIS

2015 Energy management with the support of dynamic pricing strategies in real micro-grid scenarios
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Severini, M.; Squartini, S.; Fagiani, M.; Piazza, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of technologies, available at demand side, a proper energy manager with the support of a pricing profile forecaster is mandatory. Although energy management and price forecasting are recurrent topics, in literature they have been addressed separately. On the other hand, in this work, the aim is to investigate how well an energy manager is able to perform in presence of data uncertainty originating from the forecasting process. On purpose, an energy and resource manager has been revised and extended in the current manuscript. Finally, it has been complemented with a price forecasting technique, based on the Extreme Learning Machine paradigm. The proposed forecaster has proven to be better performing and more robust, with respect to the most common forecasting approaches. The energy manager, as well, has proven that the energy efficiency of the residential environment can be improved significantly. Nonetheless, to achieve the theoretical optimum, forecasting techniques tailored for that purpose may be required.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230586 Collegamento a IRIS

2015 Real-Time Implementation and Performance Evaluation of Digital Control for Loudspeakers Line Arrays
APPLIED ACOUSTICS
Autore/i: Romoli, Laura; Cecchi, Stefania; Peretti, Paolo; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/229016 Collegamento a IRIS

2015 A floor acoustic sensor for fall classification
138th Audio Engineering Society Convention 2015
Autore/i: Principi, E.; Olivetti, P.; Squartini, S.; Bonfigli, R.; Piazza, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The interest in assistive technologies for supporting people at home is constantly increasing, both in academia and industry. In this context, the authors propose a fall classification system based on an innovative acoustic sensor that operates similarly to stethoscopes and captures the acoustic waves transmitted through the floor. The sensor is designed to minimize the impact of aerial sounds in recordings, thus allowing a more focused acoustic description of fall events. In this preliminary work, the audio signals acquired by means of the sensor are processed by a fall recognition algorithm based on Mel-Frequency Cepstral Coefficients, Supervectors and Support Vector Machines, to discriminate among different types of fall events. The performance of the algorithm has been evaluated against a specific audio corpus comprising falls of persons and of common objects. The results show the effectiveness of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230581 Collegamento a IRIS

2015 Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview
2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
Autore/i: Bonfigli, R.; Squartini, S.; Fagiani, M.; Piazza, F.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Research on Smart Grids has recently focused on the energy monitoring issue, with the objective to maximize the user consumption awareness in building contexts on one hand, and to provide a detailed description of customer habits to the utilities on the other. One of the hottest topic in this field is represented by Non-Intrusive Load Monitoring (NILM): it refers to those techniques aimed at decomposing the consumption aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. The focus here is on unsupervised algorithms, which are the most interesting and of practical use in real case scenarios. Indeed, these methods rely on a sustainable amount of a-priori knowledge related to the applicative context of interest, thus minimizing the user intervention to operate, and are targeted to extract all information to operate directly from the measured aggregate data. This paper reports and describes the most promising unsupervised NILM methods recently proposed in the literature, by dividing them into two main categories: load classification and source separation approaches. An overview of the public available dataset used on purpose and a comparative analysis of the algorithms performance is provided, together with a discussion of challenges and future research directions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230591 Collegamento a IRIS

2015 A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments
NEUROCOMPUTING
Autore/i: Fagiani, M.; Squartini, S; Gabrielli, L; Spinsante,S; Piazza, F
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, experiments concerning the prediction of water and natural gas consumption are presented, focusing on how to exploit data heterogeneity to get a reliable outcome. Prior to this, an up-to-date state-of-the-art review on the available datasets and forecasting techniques of water and natural gas consumption, is conducted. A collection of techniques (Artificial Neural Networks, Deep Belief Networks, Echo State Networks, Support Vector Regression, Genetic Programming and Extended Kalman Filter-Genetic Programming), partially selected from the state-of-the-art ones, are evaluated using the few publicly available datasets. The tests are performed according to two key aspects: homogeneous evaluation criteria and application of heterogeneous data. Experiments with heterogeneous data obtained combining multiple types of resources (water, gas, energy and temperature), aimed to short-term prediction, have been possible using the Almanac of Minutely Power dataset (AMPds). On the contrary, the Energy Information Administration (E.I.A.) data are used for long-term prediction combining gas and temperature information. At the end, the selected approaches have been evaluated using the sole Tehran water consumption for long-term forecasts (thanks to the full availability of the dataset). The AMPds and E.I.A. natural gas results show a correlation with temperature, that produce a performance improvement. The ANN and SVR approaches achieved good performance for both long/short-term predictions, while the EKF-GP showed good outcomes with the E.I.A. datasets. Finally, it is the authors' purpose to create a valid starting point for future works that aim to develop innovative forecasting approaches, providing a fair comparison among different computational intelligence and machine learning techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230155 Collegamento a IRIS

2015 Computational Energy Management in Smart Grids
NEUROCOMPUTING
Autore/i: Squartini, S.; Liu, D.; Piazza, F.; Zhao, D.; He, H.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230588 Collegamento a IRIS

2015 A Deep Neural Network approach for Voice Activity Detection in multi-room domestic scenarios
Proceedings of the International Joint Conference on Neural Networks
Autore/i: Ferroni, G.; Bonfigli, R.; Principi, E.; Squartini, S.; Piazza, F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a Voice Activity Detector (VAD) for multi-room domestic scenarios. A multi-room VAD (mVAD) simultaneously detects the time boundaries of a speech segment and determines the room where it was generated. The proposed approach is fully data-driven and is based on a Deep Neural Network (DNN) pre-trained as a Deep Belief Network (DBN) and fine-tuned by a standard error back-propagation method. Six different types of feature sets are extracted and combined from multiple microphone signals in order to perform the classification. The proposed DBN-DNN multi-room VAD (simply referred to as DBN-mVAD) is compared to other two NN based mVADs: a Multi-Layer Perceptron (MLP-mVAD) and a Bidirectional Long Short-Term Memory recurrent neural network (BLSTM-mVAD). A large multi-microphone dataset, recorded in a home, is used to assess the performance through a multi-stage analysis strategy comprising multiple feature selection stages alternated by network size and input microphones selections. The proposed approach notably outperforms the alternative algorithms in the first feature selection stage and in the network selection one. In terms of area under precision-recall curve (AUC), the absolute increment respect to the BLST-mVAD is 5.55%, while respect to the MLP-mVAD is 2.65%. Hence, solely the proposed approach undergoes the remaining selection stages. In particular, the DBN-mVAD achieves significant improvements: in terms of AUC and F-measure the absolute increments are equal to 10.41% and 8.56% with respect to the first stage of DBN-mVAD.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230585 Collegamento a IRIS

2015 Short-term Load Forecasting for Smart Water and Gas Grids: a comparative evaluation
2015 IEEE 15th International Conference on Environment and Electrical Engineering, EEEIC 2015 - Conference Proceedings
Autore/i: Fagiani, M.; Squartini, S.; Bonfigli, R.; Piazza, F.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Moving from a recent publication of Fagiani et al. [1], short-term predictions of water and natural gas consumption are performed exploiting state-of-the-art techniques. Specifically, for two datasets, the performance of Support Vector Regression (SVR), Extreme Learning Machine (ELM), Genetic Programming (GP), Artificial Neural Networks (ANNs), Echo State Networks (ESNs), and Deep Belief Networks (DBNs) are compared adopting common evaluation criteria. Concerning the datasets, the Almanac of Minutely Power Dataset (AMPds) is used to compute predictions with domestic consumption, 2 year of recordings, and to perform further evaluations with the available heterogeneous data, such as energy and temperature. Whereas, predictions of building consumption are performed with the datasets recorded at the Department for International Development (DFID). In addition, the results achieved for the previous release of the AMPds, 1 year of recordings, are also reported, in order to evaluate the impact of seasonality in forecasting performance. Finally, the achieved results validate the suitability of ANN, SVR and ELM approaches for prediction applications in small-grid scenario. Specifically, for the domestic consumption the best performance are achieved by SVR and ANN, for natural gas and water, respectively. Whereas, the ANN shows the best results for both water and natural gas forecasting in building scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230592 Collegamento a IRIS

2015 Acoustic Template-Matching for Automatic Emergency State Detection: an ELM based algorithm
NEUROCOMPUTING
Autore/i: E. Principi; S. Squartini; E. Cambria; F. Piazza
Classificazione: 1 Contributo su Rivista
Abstract: Extreme Learning Machine (ELM) represents a popular paradigm for training feedforward neural networks due to its fast learning time. This paper applies the technique for the automatic classification of speech utterances. Power Normalized Cepstral Coefficients (PNCC) are employed as feature vectors and ELM performs the final classification. Both the baseline ELM algorithm and ELM with kernel have been employed and tested. Due to the fixed number of input neurons in the ELM, a length normalization algorithm is employed to transform the PNCC sequence into a vector of fixed length. Length normalization has been performed using two techniques: the first is based on Dynamic Time Warping (DTW) distances, the second on the vectorized outerproduct of trajectory matrix. Experiments have been conducted on the TIDIGITS corpus, to assess the performance on an isolated speech recognition task, and on ITAAL, to validate the system in an emergency detection task in realistic acoustic conditions. The ELM approach has been compared to template matching based on Dynamic Time Warping and to a Support Vector Machine based speech recognizer. The obtained results demonstrated the effectiveness of the approach both in terms of recognition performance and execution times. In particular, classification based on PNCCs, DTW distances and ELM kernel resulted in the best performing algorithm both in terms of recognition accuracy and execution times.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/153902 Collegamento a IRIS

2015 Signer Independent Isolated Italian Sign Recognition Based on Hidden Markov Models
PATTERN ANALYSIS AND APPLICATIONS
Autore/i: M. Fagiani; E. Principi; S. Squartini; F. Piazza
Classificazione: 1 Contributo su Rivista
Abstract: Sign languages represent the most natural way to communicate for deaf and hard of hearing. However, there are often barriers between people using this kind of languages and hearing people, typically oriented to express themselves by means of oral languages. In order to facilitate the social inclu- siveness in everyday life for deaf minorities, technology can play an impor- tant role. Indeed many attempts have been recently made by the scientific community to develop automatic translation tools. Unfortunately, not many solutions are actually available for the Italian Sign Language (Lingua Italiana dei Segni - LIS) case study, specially for what concerns the recognition task. In this paper the authors want to face such a lack, in particular addressing the signer-independent case study, i.e., when the signers in the testing set are to included in the training set. From this perspective, the proposed algorithm represents the first real attempt in the LIS case. The automatic recognizer is based on Hidden Markov Models (HMMs) and video features have been extracted by using the OpenCV open source library. The effectiveness of the HMM system is validated by a comparative evaluation with Support Vector Machine approach. The video material used to train the recognizer and testing its performance consists in a database that the authors have deliberately cre- ated by involving ten signers and 147 isolated-sign videos for each signer. The database is publicly available. Computer simulations have shown the effective- ness of the adopted methodology, with recognition accuracies comparable to those obtained by the automatic tools developed for other sign languages.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/179303 Collegamento a IRIS

2015 An integrated system for voice command recognition and emergency detection based on audio signals
EXPERT SYSTEMS WITH APPLICATIONS
Autore/i: Principi, E.; Squartini, S.; Ferroni, G.; Bonfigli, R.; Piazza, F.
Classificazione: 1 Contributo su Rivista
Abstract: The recent reports on population ageing in the most advanced countries are driving governments and the scientific community to focus on technologies for providing assistance to people in their own homes. Particular attention has been devoted to solutions based on acoustic signals since they provide a convenient way to monitor people activities and they enable hands-free human–machine interfaces. In this context, this paper presents a complete solution for voice command recognition and emergency detection based on audio signals entirely integrated in a low-consuming embedded platform. The system combines an active operation mode were distress calls are captured and a vocal interface is enabled for controlling the home automation subsystem, and a pro-active mode, were a novelty detection algorithm detects abnormal acoustic events to alert the user of a possible emergency. In the first operation mode, a Voice Activity Detector captures voice segments of the audio signal, and a speech recogniser detects commands and distress calls. In the pro-active mode, an acoustic novelty detector is employed in order to be able to deal with unknown sounds, thus not requiring an explicit modelling of emergency sounds. In addition, the system integrates a VoIP infrastructure so that emergencies can be communicated to relatives or care centres. The monitoring unit is equipped with multiple microphones and it is connected to the home local area network to communicate with the home automation subsystem. The algorithms have been implemented in a low-consuming embedded platform based on a ARM Cortex-A8 CPU. The effectiveness of the adopted algorithms has been tested on two different databases: ITAAL and A3Novelty. The obtained results show that the adopted solutions are suitable for speech and audio event monitoring in a realistic scenario.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224934 Collegamento a IRIS

2015 Domestic Water and Natural Gas Demand Forecasting by using Heterogeneous Data: A Preliminary Study
Advances in Neural Networks: Computational and Theoretical Issues
Autore/i: Fagiani, M.; Squartini, S.; Gabrielli, L.; Spinsante, S.; Piazza, F.
Editore: Springer Science and Business Media Deutschland GmbH
Classificazione: 2 Contributo in Volume
Abstract: In this paper a preliminary study concerning prediction of domestic consumptions of water and natural gas based on genetic pro- gramming (GP) and its combination with extended Kalman Filter (EKF) is presented. The used database (AMPds) are composed of power, water, natural gas consumptions and temperatures. The study aims to investi- gate novel solutions and adopts state-of-the-art approaches to forecast resource demands using heterogeneous data of an household scenario. In order to have a better insight of the prediction performance and properly evaluate possible correlation between the various data types, the GP ap- proach has been applied varying the combination of input data, the time resolution, the number of previous data used for the prediction (lags) and the maximum depth of the tree. The best performance for both water and natural gas prediction have been achieved using the results obtained by the GP model created for a time resolution of 24 h, and using a set of input data composed of both water and natural gas consumptions. The results confirm the presence of a strong correlation between natural gas and water consumptions. Additional experiments have been executed in order to evaluate the effect of the prediction performance using long period heterogeneous data, obtained from the U.S. Energy Information Administration (E.I.A.).
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/225146 Collegamento a IRIS

2015 Unexpected Thermal Properties of Water Diffusion in Very Porous Materials
WATER
Autore/i: Signanini, P; De Santis, A; Di Fazio, M; Greco, P; Merla, A; Monosi, S; Piazza, F; Rainone, Ml; Fenzi, F; Torrese, P
Classificazione: 1 Contributo su Rivista
Abstract: Significant and persistent decreases in temperature have been observed in very porous materials when they are partially immersed in water at room temperature. As the sample sizes were much smaller than the maximum height of the typical capillary rise, this represents a far-from-equilibrium system. We attribute this thermal decrease to two concurrent actions: (i) the highly porous property of the material used; and (ii) a transition-phase-like process of the water. Thus, the water not only cools down the material surface through evaporation at the sample–air interface, but it also expands within the material, causing a further internal decrease in temperature that cannot be explained solely through evaporation. This latter process is persistent enough to maintain the decrease in temperature over time. This unexpected characteristic of water and its persistence when diffusing inside an extremely porous medium are the most original results of this study. Our results seem in agreement with the recent model on the fourth phase of water by Pollack.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/234177 Collegamento a IRIS

2015 Energy-Aware Task Scheduler for Self-Powered Sensor Nodes: from Model to Firmware
AD HOC NETWORKS
Autore/i: Severini, Marco; Squartini, Stefano; Piazza, Francesco; Conti, Massimo
Classificazione: 1 Contributo su Rivista
Abstract: The self-powering paradigm surely represents a challenging issue in the Wireless Sensor Network (WSN) field. The chance of supplying the sen- sor node with environmental energy is attractive not only to make it au- tonomous and reduce the human intervention for battery substitution but also to improve the overall WSN flexibility and applicability. From this same perspective, a relevant role is played by the node task scheduling, which is required to face strong constraints due to energy availability limitations. On purpose, some of the authors have recently proposed an Energy-Aware ap- proach (namely Energy-Aware Lazy Scheduling Algorithm, EA-LSA), which shows a certain versatility in efficiently exploiting the harvested energy, re- ducing the starving occurrences in non-ideal conditions, with respect to the no-EA counterpart. In this work a task scheduler based on the EA-LSA technique has been implemented on a low-cost and commercially available platform, from Texas Instruments, with the aim to propose an open and easy-to-use HW/SW ref- erence for the community working in the field. The firmware, including the hardware abstraction and the scheduling routine layers, has been developed from scratch. Moreover, a few tasks have been developed so that the device activity could be simulated in a realistic scenario. To monitor the harvesting process an emulated energy harvester has been devised and included in the overall framework. The different task energy consumptions have been measured by means of an energy audit tool, specifically set up in order to properly gauge the very low current consumptions of the device. Experiments performed under different power income levels, allowed to positively conclude about the effectiveness of the implemented Energy-Aware scheduler, its flexibility in terms of environmental energy exploitation, and therefore its suitability for involvement in real-world WSNs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/174308 Collegamento a IRIS

2015 A Voice Activity Detection Algorithm for Multichannel Acoustic Echo Cancellation Exploiting Fundamental Frequency Estimation
Proc. 9th Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: Romoli, Laura; Cecchi, Stefania; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233959 Collegamento a IRIS

2015 Investigation on Crosstalk Cancellation introducing Loudspeakers Directivity in HRTFs approximation
Proc. 9th Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: Cecchi, Stefania; Romoli, Laura; Piazza, Francesco; Bettarelli, Ferruccio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233960 Collegamento a IRIS

2015 An Optimization Procedure for a Nonlinear System Identification Approach based on Cubic Splines
Proc. Int. Conf. Electronics, Computers and Artificial Intelligence
Autore/i: Romoli, Laura; Cecchi, Stefania; Gasparini, Michele; Piazza, Francesco
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/233958 Collegamento a IRIS

2014 Smart Home Task and Energy Resource Scheduling based on Nonlinear Programming
Recent Advances of Neural Network Models and Applications
Autore/i: M. Severini; S. Squartini; G. Surace; F. Piazza
Editore: Springer Verlag Germany:Tiergartenstrasse 17, D 69121 Heidelberg Germany:011 49 6221 3450, EMAIL: g.braun@springer.de, INTERNET: http://www.springer.de, Fax: 011 49 6221 345229
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/127267 Collegamento a IRIS

2014 BAR LIS: a web application for Italian Sign Language based interaction
AMBIENT ASSISTED LIVING
Autore/i: Luca Nardi; Matteo Rubini; Stefano Squartini; Emanuele Principi; Francesco Piazza
Editore: Springer Verlag Germany:Tiergartenstrasse 17, D 69121 Heidelberg Germany:011 49 6221 3450, EMAIL: g.braun@springer.de, INTERNET: http://www.springer.de, Fax: 011 49 6221 345229
Classificazione: 2 Contributo in Volume
Abstract: This work presents the development and testing of BAR LIS (BAR in Italian Sign Language), a web application created in collaboration with the Ancona division of Ente Nazionale Sordi (ENS) and presented during X Masters Awards 2012 event in Senigallia, Italy. BAR LIS was structured as a small dictionary of words and related signs in LIS, Italian Sign Language (presented as 3D rendered animations) in order to ease communication between people attending the X Masters event and hearing impaired personnel of the ENS stall, which included a small coffee shop. A more extensive set of words was later created and tested with ENS members in order to study the impact of resolution of 3D models on comprehensibility and quality of the signs.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/127262 Collegamento a IRIS

2014 Adaptive Feedback Active Noise Control for Yacht Environments
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
Autore/i: P. Peretti; S. Cecchi; L. Romoli; F. Piazza
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124471 Collegamento a IRIS

2014 Learning capabilities of ELM-trained Time-Varying Neural Networks
Recent Advances of Neural Network Models and Applications
Autore/i: S. Squartini; Y. Ye; F. Piazza
Editore: Springer Verlag Germany:Tiergartenstrasse 17, D 69121 Heidelberg Germany:011 49 6221 3450, EMAIL: g.braun@springer.de, INTERNET: http://www.springer.de, Fax: 011 49 6221 345229
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/127266 Collegamento a IRIS

2014 Improving the performance of a in-home acoustic monitoring system by integrating a vocal effort classification algorithm
Proceedings of AES 136th Convention
Autore/i: E. Principi; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/153910 Collegamento a IRIS

2014 A Real-Time Implementation of an Acoustic Novelty Detector on the BeagleBoard-xM
Proceedings of EDERC2014
Autore/i: R. Bonfigli; G. Ferroni; E. Principi; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Novelty detection consists in recognising events that deviate from normality. This paper presents the implementation of a real-time statistical novelty detector on the BeagleBoard-xM. The application processes an incoming audio signal, extracts Power Normalized Cepstral Coefficients and determines whether a novelty sound is present or not based on a statistical model of normality. The novelty detector has been implemented as a standalone graphical application capable of running in real-time on the BeagleBoard-xM platform. Experiments have been conducted to assess the performance of the solution in terms of both detection performance and of real-time capabilities. The results demonstrate that the system is able to operate in real-time on the BeagleBoard-xM with a real-time factor equal to 8.10%, and an F-Measure equal to 77.41%.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/179903 Collegamento a IRIS

2014 Power Normalized Cepstral Coefficients based supervectors and i-vectors for small vocabulary speech recognition
Proceedings of IJCNN2014
Autore/i: E. Principi; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Template-matching and discriminative techniques, like support vector machines (SVMs), have been widely used for automatic speech recognition. Both methods require that varying length sequences are mapped to vectors of fixed lengths: in template-matching, the problem is solved by means of dynamic time warping (DTW), while in SVM with dynamic kernels. The supervector and i-vector paradigms seem to represent a valid solution to such a problem when SVM are employed for classification. In this work, Gaussian mean supervectors (GMS), Gaussian posterior probability supervectors (GPPS) and i-vectors are evaluated as features both for template-matching and for SVM-based speech recognition in a comparative fashion. All these features are based on Power Normalized Cepstral Coefficients (PNCCs) directly extracted from speech utterances. The different methods are assessed in small vocabulary speech recognition tasks using two distinct corpora, and they have been compared to DTW, dynamic time alignment kernel (DTAK), outerproduct of trajectory matrix, and PocketSphinx as further recognition techniques to be evaluated. Experimental results showed the appropriateness of the supervector and i-vector based solutions with respect to the other state-of-the art techniques here addressed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/153906 Collegamento a IRIS

2014 Energy Demand Management Through Uncertain Data Forecasting: An Hybrid Approach
Frontiers of Intelligent Control and Information Processing
Autore/i: M. Severini; S. Squartini; F. Piazza
Editore: World Scientific Publishing Company
Classificazione: 2 Contributo in Volume
Abstract: Although Smart Grids may represent the solution to the limits of nowa- days Power Grid, the turnover may not occur in the next future yet due the complex nature of energy distribution. Thus, as a more short term effort, to improve the responsiveness of the energy demand to the power grid load, more and more energy providers apply dynamic pricing schemes for grid users. Believing that dynamic pricing policies may be an effective asset even at a micro-grid level, an hybrid en- ergy management scheme is proposed in this contribution. While the nonlinear nature of a micro grid, involving the task allocation and the thermal constraint satisfaction, can be modeled as a mixed integer nonlinear programming problem, neural-network forecasting abilities can provide a sustainable support under real- istic operating conditions. Based on the forecast of solar energy production and grid energy prices and outdoor temperature, the optimization of tasks allocation is aimed to lower both the user costs and the grid burden while accounting the ther- mal comfort of the user. Through computer simulations, whose degree of realism is enhanced by the adoption of forecast data, the shift of the grid load towards low energy price hours is confirmed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/136663 Collegamento a IRIS

2014 An advanced spatial sound reproduction system with listener position tracking
Proc. 22nd European Signal Processing Conference
Autore/i: Cecchi S.; Primavera A.; Virgulti M.; Bettarelli F.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/180306 Collegamento a IRIS

2014 A Multichannel and Multiple Position Adaptive Room Response Equalizer in Warped Domain: Real-time Implementation and Performance Evaluation
APPLIED ACOUSTICS
Autore/i: Cecchi S.; Romoli L.; Carini A.; Piazza F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148309 Collegamento a IRIS

2014 A Novel Decorrelation Approach for an Advanced Multichannel Acoustic Echo Cancellation System
Proc. 22nd European Signal Processing Conference
Autore/i: Romoli L.; Cecchi S.; Comminiello D.; Piazza F.; Uncini A.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/180304 Collegamento a IRIS

2014 Computational Framework based on Task and Resource Scheduling for Micro Grid Design
Proceedings of IJCNN2014
Autore/i: M. Severini; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Within micro grid scenarios, optimal energy management represents an important paradigm to improve the grid efficiency while lowering its burden. While usually real time energy management is considered, an offline approach can be also adopted to maximize the grid efficiency in certain contexts. Indeed, by evaluating the energy management performance according to the user needs, it is possible to asses which technologies allow the overall system to operate at its best, given the expected load level. From this perspective, a computational framework based on the "Mixed-Integer Linear Programming" paradigm has been proposed in this paper as a tool to simulate the micro grid behaviour in terms of energy consumption and in dependence on the technology of choice. By modelling the energy production and storage means, the pool of electricity tasks, and the thermal behaviour of the building, suitable energy management policies for the micro grid scenario under study can be developed and tested in different operating conditions and time horizons. Moreover, the forecasting paradigm has been integrated into the framework to deal with data uncertainty, and a Neural Network approach has been employed on purpose. Performed computer simulations, related to a six-apartments building scenario, have proven that the suggested framework can fruitfully be adopted to assess the effectiveness of different technical solutions in terms of overall energy cost, thus supporting the decisional process occurring during the micro grid design.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/153907 Collegamento a IRIS

2014 Advanced Integration of Multimedia Assistive Technologies: a prospective outlook
Proceedings of MESA 2014
Autore/i: D. Liciotti; G. Ferroni; E. Frontoni; S. Squartini; E. Principi; R. Bonfigli; P. Zingaretti; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In the recent years several studies on population ageing in the most advanced countries argued that the share of people older than 65 years is steadily increasing. In order to tackle this phenomena, a significant effort has been devoted to the development of advanced technologies for supervising the domestic environments and their inhabitants to provide them assistance in their own home. In this context, the present paper aims to delineate a novel, highly-integrated system for advanced analysis of human behaviours. It is based on the fusion of the audio and vision frameworks, developed at the Multimedia Assistive Technology Laboratory (MATeLab) of the Università Politecnica delle Marche, in order to operate in the ambient assisted living context exploiting audio-visual domain features. The existing video framework exploits vertical RGB-D sensors for people tracking, interaction analysis and users activities detection in domestic scenarios. The depth information has been used to remove the affect of the appearance variation and to evaluate users activities inside the home and in front of the fixtures. In addition, group interactions are monitored and analysed. On the other side, the audio framework recognises voice commands by continuously monitoring the acoustic home environment. In addition, a hands-free communication to a relative or to a healthcare centre is automatically triggered when a distress call is detected. Echo and interference cancellation algorithms guarantee the high-quality communication and reliable speech recognition, respectively. The system we intend to delineate, thus, exploits multi-domain information, gathered from audio and video frameworks each, and stores them in a remote cloud for instant processing and analysis of the scene. Related actions are consequently performed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/179906 Collegamento a IRIS

2014 Neural Networks Based Methods for Voice Activity Detection in a Multi-room Domestic Environment
Proceedings of the First Italian Conference on Computational Linguistics CLiC-it 2014 & the Fourth International Workshop EVALITA 2014
Autore/i: G. Ferroni; R. Bonfigli; E. Principi; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: A plethora of Voice or Speaker Activity Detection systems exist in literature. They are indeed a fundamental part of complex systems that deals with speech processing. In this work the authors exploit neural network based VAD to address the speaker activity detection in a multi-room domestic scenario. The goal is to detect the voice activity in each of the two target rooms in presence of other sounds and speeches occurring in other rooms and outside. A large dataset recorded in a smart-home is provided and result obtained are acceptable.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/205118 Collegamento a IRIS

2014 Riconoscimento Automatico di Richieste di Aiuto e Comandi di Domotica per Ambient Assisted Living
X Convegno Nazionale dell'Associazione Italiana di Scienze della Voce
Autore/i: E. Principi; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/205117 Collegamento a IRIS

2014 A low latency implementation of a non-uniform partitioned convolution algorithm for room acoustic simulation
SIGNAL, IMAGE AND VIDEO PROCESSING
Autore/i: Primavera A.; Cecchi S.; Romoli L.; Peretti P.; Piazza F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/87664 Collegamento a IRIS

2014 Wireless M-Bus Sensor Networks for Smart Water Grids: Analysis and Results
INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS
Autore/i: Spinsante, Susanna; Squartini, Stefano; Gabrielli, Leonardo; M., Pizzichini; Gambi, Ennio; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Abstract: Wireless sensor network technologies are experiencing an impressive development, as they represent one of the building blocks upon which new paradigms, such as Internet of Things and Smart Cities, may be implemented. Among the different applications enabled by such technologies, automatic monitoring of the water grid, and smart metering of water consumptions, may have a great impact on the preservation of one of the most valued, and increasingly scarce, natural resources. Sensor nodes located along water pipes cannot rely on the availability of power grid facilities to get the necessary energy imposed by their working conditions. In this sense, an energy-efficient design of the network architecture, and the evaluation of Energy Harvesting techniques to sustain its nodes, becomes of paramount importance. This paper investigates the suitability of a Wireless Metering Bus-based solution for the implementation of smart water grids, by evaluating network and node related performance, through simulations, prototype design, and experimental tests, which confirm the feasibility and efficiency of the proposal.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/171903 Collegamento a IRIS

2014 Objective and Subjective Investigation on a Novel Method for Digital Reverberator Parameters Estimation
IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
Autore/i: Primavera, Andrea; Cecchi, Stefania; Junfeng, Li; Piazza, Francesco
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148302 Collegamento a IRIS

2014 AN EFFICIENT IMPLEMENTATION OF ACOUSTIC CROSSTALK CANCELLATION FOR 3D AUDIO RENDERING
Proc. 2nd IEEE China Summit and International Conference on Signal and Information Processing
Autore/i: Cecchi S.; Primavera A.; Virgulti M.; Bettarelli F.; Li J.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163719 Collegamento a IRIS

2014 A Novel Decorrelation Approach for Multichannel System Identification
Proc. {IEEE} International Conference on Acoustics, Speech and Signal Processing
Autore/i: Romoli L.; Cecchi S.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148306 Collegamento a IRIS

2014 AN EFFICIENT TIME VARYING HYBRID REVERBERATOR FOR ROOM ACOUSTIC SIMULATION
Proc. 2nd IEEE China Summit and International Conference on Signal and Information Processing
Autore/i: Primavera A.; Cecchi S.; Piazza F.; Li J.; Yan Y.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163720 Collegamento a IRIS

2014 Advanced Audio Spatializer combined with a Multipoint Equalization System
Proc. IEEE World Congress on Computational Intelligence
Autore/i: Cecchi S.; Primavera A.; Piazza F.; Bettarelli F.; Li J.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163717 Collegamento a IRIS

2014 A Novel Approach for Prototype Extraction in a Multipoint Equalization Procedure
Proc. 136th Audio Engineering Society Convention
Autore/i: Cecchi S.; Romoli L.; Piazza F.; Bank B.; Carini A.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148304 Collegamento a IRIS

2014 Application of common-pole parallel filters to nonlinear models based on orthogonal functions
Proc. 136th Audio Engineering Society Convention
Autore/i: Romoli L.; Cecchi S.; Bank B.; Gasparini M.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148303 Collegamento a IRIS

2014 Advanced Intelligent Acoustic Interfaces for Multichannel Audio Reproduction
Proc. IEEE World Congress on Computational Intelligence
Autore/i: Comminiello D.; Cecchi S.; Gasparini M.; Scarpiniti M.; Uncini A.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163718 Collegamento a IRIS

2014 Multiphysic modeling and heuristic optimization of compression driver design
Proc. 136th Audio Engineering Society Convention
Autore/i: Gasparini M.; Cecchi S.; Piazza F.; Capucci E.; Toppi R.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/148305 Collegamento a IRIS

2013 Development of Multipoint Mixed-phase Equalization system for multiple environments
Proc. 134th Audio Engineering Society Convention
Autore/i: S. Cecchi; M. Virgulti; F. Bettarelli; S. Doria; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124472 Collegamento a IRIS

2013 IIR Filter Approximation of an Innovative Digital Audio Equalizer
Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: M. Virgulti; S. Cecchi; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124466 Collegamento a IRIS

2013 A Multichannel and Multiple Position Adaptive Room Response Equalizer in Warped Domain
Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: S. Cecchi; L. Romoli; F. Piazza; A. Carini
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124469 Collegamento a IRIS

2013 Hybrid Reverberation Algorithm: a Practical Approach
AIA-DAGA Conference
Autore/i: A. Primavera; M. Gasparini; S. Cecchi; L. Romoli; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124464 Collegamento a IRIS

2013 Identification of Hammerstein model using Cubic Splines and FIR filtering
Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: M. Gasparini; L. Romoli; S. Cecchi; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124468 Collegamento a IRIS

2013 Evaluation of a Channel Decorrelation Approach for Stereo Acoustic Echo Cancellation
Int'l Symposium on Image and Signal Processing and Analysis
Autore/i: L. Romoli; S. Cecchi; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124467 Collegamento a IRIS

2013 A Combined Approach for Channel Decorrelation in Stereo Acoustic Echo Cancellation Exploiting Time-Varying Frequency Shifting
IEEE SIGNAL PROCESSING LETTERS
Autore/i: L. Romoli; S. Cecchi; F. Piazza
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124470 Collegamento a IRIS

2013 A distributed system for recognizing home automation commands and distress calls in the Italian language
Proceedings of Interspeech 2013
Autore/i: E. Principi; S. Squartini; F. Piazza; D. Fuselli; M. Bonifazi
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper describes a system for recognizing distress calls and home automation voice commands in a smart-home. Distress calls are recognized with the purpose of assisting people in their own homes: when they are detected, a phone call is automatically established with a contact in a address book and the person can request for assistance. The voice call is established through a voice over ip stack, with hands-free communication guaranteed by an acoustic echo canceller. The acoustic environment is constantly monitored by several low-consuming devices distributed throughout the home. In each device, a voice activity detector detects speech segments, and a speech recognition engine recognizes commands and distress calls. Robustness to environmental disturbances has been increased by employing Power Normalized Cepstral Coefficients and by using an adaptive algorithm for interference cancellation. An Italian speech corpus of home automation commands and distress calls has been developed for evaluation purposes. The corpus has been recorded in a real room using multiple microphones, and each sentence has been uttered both in normal and shouted speaking styles. The system performance has been assessed in terms of commands/distress recognition accuracy in order to prove the effectiveness of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/112686 Collegamento a IRIS

2013 Hybrid Soft Computing algorithmic framework for Smart Home Energy Management
SOFT COMPUTING
Autore/i: M. Severini; S. Squartini; F. Piazza
Classificazione: 1 Contributo su Rivista
Abstract: Energy management in Smart Home environments is undoubtedly one of the pressing issues in the Smart Grid research field. The aim typically consists in developing a suitable engineering solution able to maximally exploit the availability of renewable resources. Due to the presence of diverse cooperating devices, a complex model, involving the characterization of nonlinear phenomena, is indeed required on purpose. In this paper an Hybrid Soft Computing algorithmic framework, where genetic, neural networks and deterministic optimization algorithms jointly operate, is proposed to perform an efficient scheduling of the electrical tasks and of the activity of energy resources, by adequately handling the inherent nonlinear aspects of the energy management model. In particular, in order to address the end-user comfort constraints, the home thermal characterization is needed: this is accomplished by a nonlinear model relating the energy demand with the required temperature profile. A genetic algorithm, based on such model, is then used to optimally allocate the energy request to match the user thermal constraints, and therefore to allow the mixed-integer deterministic optimization algorithm to determine the remaining energy management actions. From this perspective, the ability to schedule the tasks and allocate the overall energy resources over a finite time horizon is assessed by means of diverse computer simulations in realistic conditions, allowing the authors to positively conclude about the effectiveness of the proposed approach. The degree of realism of the simulated scenario is confirmed by the usage of solar energy production forecasted data, obtained by means of a neural-network based algorithm which completes the framework.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/112685 Collegamento a IRIS

2013 Advancements and Performance analysis on the Wireless Music Studio (WeMUST) framework
AES 134th Convention
Autore/i: L. Gabrielli; S. Squartini; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Music production devices and musical instruments can take advantage of IEEE 802.11 wireless networks for interconnection and audio data sharing. In previous works such networks have been proved able to support high-quality audio streaming between devices at acceptable latencies, in several application scenarios. In this work, a prototype device discovery mechanism is described to improve ease of use and flexibility. A diagnostic tool is also described and provided to the community which allows to characterize average network latency and packet loss. Lower latencies are reported after software optimization and sustainability of multiple audio channels is also proved by means of experimental tests.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/91066 Collegamento a IRIS

2013 Optimization Algorithms for Home Energy Resource Scheduling in presence of data uncertainty
Proceedings of ICICIP 2013
Autore/i: Stefano Squartini; Matteo Boaro; Francesco De Angelis; Danilo Fuselli; Francesco Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Smart Home Energy Management is a very hot topic for the scientific community and some interesting solutions have also recently appeared on the market. One key issue is represented by the capability of planning the usage of energy resources in order to reduce the overall energy costs. This means that, considering the dynamic electricity price and the availability of adequately sized storage system, the expert system is supposed to automatically decide the more convenient policy for energy management from and towards the grid. In this work a comparison among different linear and nonlinear methods for home energy resource scheduling is proposed, considering the presence of data uncertainty into account. Indeed, whereas the employment of advanced optimization frameworks can take advantage by their inherent offline approach, the need to forecast the energy price and the amount of self-generated power. A residential scenario, in which a system storage and renewable resources are available and exploitable to match the user load demand, has been considered for performed computer simulations: obtained results show how the offline approaches provide good performance also in presence of uncertain data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/91061 Collegamento a IRIS

2013 An Embedded-processor driven Test Bench for Acoustic Feedback Cancellation in real environments
AES 134th Convention
Autore/i: F. Faccenda; S. Squartini; E. Principi; L. Gabrielli; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In order to facilitate the communication among speakers, speech reinforcement systems equipped with microphones and loudspeakers are employed. Due to the acoustic couplings between them, the speech intelligibility may result ruined and, moreover, high channel gains could drive the system to instability. Acoustic Feedback Cancellation (AFC) methods need to be applied to keep the system stable. In this work, a new Test Bench for testing AFC algorithms in real environments is proposed. It is based on the TMS320C6748 processor, running the Suppressor-PEM algorithm, a recent technique based on the PEM-AFROW paradigm. The partitioned block frequency domain adaptive filter (PB-FDAF) paradigm has been adopted to keep the computational complexity low. A professional sound card and a PC, where an automatic gain controller has been implemented to prevent signal clipping, complete the framework. Several experimental tests confirmed the framework suitability to operate under diverse acoustic conditions.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/91064 Collegamento a IRIS

2013 A Speech-Based System for In-Home Emergency Detection and Remote Assistance
AES 134th Convention
Autore/i: E. Principi; D. Fuselli; S. Squartini; M. Bonifazi; F. Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper describes a system for the detection of emergency states and for the remote assistance of people in their own homes. Emergencies are detected recognizing distress calls by means of a speech recognition engine. When an emergency is detected, a phone call is automatically established with a relative or friend by means of a VoIP stack and an Acoustic Echo Canceller. Several low-consuming embedded units are distributed throughout the house to monitor the acoustic environment, and one central unit coordinates the system operation. This unit also integrates multimedia content delivery services, and home automation functionalities. Being an ongoing project, this paper describes the entire system and then focuses on the algorithms implemented for the acoustic monitoring and the hands-free communication services. Preliminary experiments have been conducted to assess the performance of the recognition module in noisy and reverberated environments, and the out of grammar rejection capabilities. Results showed that the implemented Power Normalized Cepstral Coefficients extraction pipeline improves the word recognition accuracy in noisy and reverberated conditions, and that introducing a "garbage phone" in the acoustic model allows to effectively reject out of grammar words and sentences.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/91067 Collegamento a IRIS

2013 Wireless M-Bus Sensor Nodes in Smart Water Grids: the Energy Issue
Proceedings of ICICIP 2013
Autore/i: Stefano Squartini; Leonardo Gabrielli; Matteo Mencarelli; Mirco Pizzichini; Susanna Spinsante; Francesco Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Smart Metering is one of the key issues in modern energy efficiency technologies. Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission, and the Metering-Bus (M-Bus) is a relevant standard example, with a wide diffusion in the European market. This paper deals with its wireless evolution, namely Wireless M-Bus (WM-Bus), and in particular looks at it from the energy consumption perspective. Indeed, specially in those applicative scenarios where the grid powering is not available, like in water and gas metering settings, it is fundamental to guarantee the sustainability of the meter itself, by means of long-life batteries or suitable energy harvesting technologies. The present work analyzes all these aspects directly referring to a specific HW/SW implementation of the WM-Bus variants, providing some useful guidelines for its application in the smart water grid context.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/91062 Collegamento a IRIS

2013 Home Energy Resource Scheduling Algorithms and their dependency on the Battery Model
Proceedings of IEEE Symposium Series on Computational Intelligence 2013
Autore/i: Stefano Squartini; Danilo Fuselli; Matteo Boaro; Francesco De Angelis; Francesco Piazza
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Smart Home Energy Management is a very hot topic within the scientific community and some interesting solutions are also available on the market. One key issue is represented by the capability of planning the usage of energy resources in order to reduce the overall energy costs. This means that, considering the dynamic electricity price and the availability of adequately sized storage system, the expert system is supposed to automatically decide the more convenient policy to administer the electrical energy flux from and towards the grid. In this work a comparison between different methods for home energy resource scheduling is proposed and analyzed from the perspective of the dependency of their performance on the employed battery model, with special focus on its capacity and charge/discharge rates. A typical grid-connected residential energy system is considered for performed computer simulations, in which a system storage and renewable resources are available and exploitable to match the user load demand. Obtained results allows the authors to provide interesting guidelines for the selection of the battery features.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/86657 Collegamento a IRIS

2013 A New System for Automatic Recognition of Italian Sign Language
Neural Nets and Surroundings
Autore/i: M. Fagiani; E. Principi; S. Squartini; F. Piazza
Editore: Springer Verlag Germany:Tiergartenstrasse 17, D 69121 Heidelberg Germany:011 49 6221 3450, EMAIL: g.braun@springer.de, INTERNET: http://www.springer.de, Fax: 011 49 6221 345229
Classificazione: 2 Contributo in Volume
Abstract: This work proposes a preliminary study of an automatic recognition system for the Italian Sign Language (Lingua Italiana dei Segni - LIS). Several other attempts have been made in the literature, but they are typically oriented to international languages. The system is composed of a feature extraction stage, and a sign recognition stage. Each sign is represeted by a single Hidden Markov Model, with parameters estimated through the resubstitution method. Then, starting from a set of features related to the position and the shape of head and hands, the Sequential Forward Selection technique has been applied to obtain feature vectors with the minimum dimension and the best recognition performance. Experiments have been performed using the cross-validation method on the Italian Sign Language Database A3LIS-147, maintaining the orthogonality between training and test sets. The obtained recognition accuracy averaged across all signers is 47.24%, which represents an encouraging result and demonstrates the effectiveness of the idea.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/83970 Collegamento a IRIS

2013 Hybrid Reverberator Using Multiple Impulse Responses for Audio Rendering Improvement
Proc. 9th Int. Conf. on Intelligent Information Hiding and Multimedia Signal Processing
Autore/i: A. Primavera; S. Cecchi; F. Piazza; Li Junfeng; Y. Yan
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/124465 Collegamento a IRIS

2013 Adaptive Dynamic Programming Algorithm for Renewable Energy Scheduling and Battery Management
COGNITIVE COMPUTATION
Autore/i: Boaro M.; Fuselli D.; De Angelis F.; Liu D. ; Wei Q.; Piazza F.
Classificazione: 1 Contributo su Rivista
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/209324 Collegamento a IRIS

2013 Ontologies for smart homes and energy management: An implementation-driven survey
Proc. Workshop on Modeling and Simulation of Cyber-Physical Energy Systems
Autore/i: Grassi M.; Nucci M.; Piazza F.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/209319 Collegamento a IRIS

2013 Ontology-Based Device Configuration and Management for Smart Homes
Smart Innovation, Systems and Technologies
Autore/i: Nucci M.; Grassi M.; Piazza F.
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/209320 Collegamento a IRIS


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