Salvatore Serrano

Biometric Identification Based on Frequency Analysis of Cardiac Sounds

The performance of traditional biometric identification systems is, as yet, unsatisfactory in certain applications. For this reason, other physiological or behavioral characteristics have recently been considered, using new electrical or physical signals linked to a persons vital signs. This paper examines the biometric characteristics of phonocardiogram (PCG) signals from cardiac auscultation. The idea is that PCG signals have specific individual characteristics that can be taken into consideration as a physiological sign used in a biometric system. More specifically, the paper proposes a preliminary study related to the identification of individuals via frequency analysis of cardiac sounds. The results, obtained using a database containing several heart sound recordings from 20 different people, confirm the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system.

Speech Emotion Classification Using Machine Learning Algorithms

The recognition of emotional states is a relatively new technique in the field of machine learning. The paper presents the study and the performance results of a system for emotion classification using the architecture of a distributed speech recognition system (DSR). The features used were extracted by the front-end ETSI Aurora eXtended of a mobile terminal in compliance with the ETSI ES 202-211 V1.1.1 standard. On the basis of the time trend of these parameters, over 3800 statistical parameters were extracted to characterize semantic units of varying length (sentences and words). Using the WEKA (Waikato Environment for Knowledge Analysis) software the most significant parameters for the classification of emotional states were selected and the results of various classification techniques were analysed. The results, obtained using both the Berlin Database of Emotional Speech (EMO-DB) and the Speech Under Simulated and Actual Stress (SUSAS) corpus, showed that the best performance is achieved using a support vector machine (SVM) trained with the sequential minimal optimization (SMO) algorithm, after normalizing and discretizing the input statistical parameters.

A low‐complexity speech‐pause detection algorithm for communication in noisy environments

The paper presents a new low-complexity algorithm for silence suppression in adverse acoustic environments. The algorithm uses a single time-domain input parameter (signal power) given to a simple matching block. The decision module adapts a series of thresholds depending on the current estimated signal-to-noise-ratio (SNR) of the signal. A series of tests carried out using a large speech database confirm a 10% improvement in pause detection performance as compared with the AMR VAD option 1 recently adopted by ETSI for 3rd-generation mobile systems. Copyright

Applying the Chinese Remainder Theorem to Data Aggregation in Wireless Sensor Networks

In WSNs low complexity techniques for reducing the amount of bits flowing throughout the network are needed to decrease the bandwidth waste and increase the network lifetime. To this purpose, spatial correlation due to the high density of the deployed devices can be exploited. In this paper we propose a novel in-network aggregation technique based on the Chinese Remainder Theorem (CRT), which exploits the well known advantages of the source coding while using a distributed approach with no need of coordination among network nodes. Also, an appropriate network coding mechanism based on the CRT is combined to source coding to enable lossless transmission from the sources to the sink.

Comparison of local lossless compression algorithms for Wireless Sensor Networks

Compression algorithms are deeply used in Wireless Sensor Networks (WSNs) for data aggregation in order to reduce energy consumption and therefore increasing network lifetime. In this paper we compare several lossless compression algorithms by means of real-world data. Moreover we present a simple and effective lossless compression algorithm that is able to outperform existing solutions and that, considering its inherent low complexity and memory requirements, is well suited for WSNs.

Data Gathering Techniques for Wireless Sensor Networks

We study the problem of data gathering in wireless sensor networks and compare several approaches belonging to different research fields; in particular, signal processing, compressive sensing, information theory, and networking related data gathering techniques are investigated. Specifically, we derived a simple analytical model able to predict the energy efficiency and reliability of different data gathering techniques. Moreover, we carry out simulations to validate our model and to compare the effectiveness of the above schemes by systematically sampling the parameter space (i.e., number of nodes, transmission range, and sparsity). Our simulation and analytical results show that there is no best data gathering technique for all possible applications and that the trade-off between energy consumptions and reliability could drive the choice of the data gathering technique to be used. In this context, our model could be a useful tool.

Trade-offs between energy saving and reliability in low duty cycle wireless sensor networks using a packet splitting forwarding technique

One of the challenging topics and design constraints in Wireless Sensor Networks (WSNs) is the reduction of energy consumption because, in most application scenarios, replacement of power resources in sensor devices might be unfeasible. In order to minimize the power consumption, some nodes can be put to sleep during idle times and wake up only when needed. Although it seems the best way to limit the consumption of energy, other performance parameters such as network reliability have to be considered. In a recent paper, we introduced a new forwarding algorithm for WSNs based on a simple splitting procedure able to increase the network lifetime. The forwarding technique is based on the Chinese Remainder Theorem and exhibits very good results in terms of energy efficiency and complexity. In this paper, we intend to investigate a trade-off between energy efficiency and reliability of the proposed forwarding scheme when duty-cycling techniques are considered too.

Adaptive V/UV speech detection based on characterization of background noise

The paper presents an adaptive system for Voiced/Unvoiced (V/UV) speech detection in the presence of background noise. Genetic algorithms were used to select the features that offer the best V/UV detection according to the output of a background Noise Classifier (NC) and a Signal-to-Noise Ratio Estimation (SNRE) system. The system was implemented, and the tests performed using the TIMIT speech corpus and its phonetic classification. The results were compared with a nonadaptive classification system and the V/UV detectors adopted by two important speech coding standards: the V/UV detection system in the ETSI ES 202 212 v1.1.2 and the speech classification in the Selectable Mode Vocoder (SMV) algorithm. In all cases the proposed adaptive V/UV classifier outperforms the traditional solutions giving an improvement of 25% in very noisy environments.

Speech Emotion Recognition Using MFCCs Extracted from a Mobile Terminal based on ETSI Front End

The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interface in man-machine applications. The paper presents a system for the recognition of emotional states based on parameters extracted at the front end of a mobile terminal according to the ETSI ES 202 050 standard. Starting from a vector of various features derived from energy and MFCCs, an approach based on genetic algorithms is used to determine a subset of features that will allow robust speech classification of 7 emotional states: anger, joy, sadness, fear, disgust, boredom and neutral

Biometric Identification based on Frequency Analysis of Cardiac Sounds

The performance of traditional biometric identification systems is as yet unsatisfactory in certain fields of application. For this reason other physiological or behavioural characteristics are recently being considered using new electrical or physical signals linked to a persons vital signs. This paper examines the biometric characteristics of PCG (phono-cardiogram) signals from cardiac auscultation. The idea is that PCG signals have specific individual characteristics that can be taken into consideration as a valid physiological sign used in a biometric system. The paper proposes an automatic system for the identification of individuals via frequency analysis of cardiac sound. The results obtained confirm the biometric properties of PCG signals, which can thus be included among the physiological signs used by an automatic identification system.