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 person's 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.

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.

The paper proposes a performance evaluation and comparison of G.729, AMR, and fuzzy voice activity detection (FVAD) algorithms. The comparison was made using objective, psychoacoustic, and subjective parameters. A highly varied speech database was also set up to evaluate the extent to which VADs depend on language, the signal-to-noise ratio (SNR), or the power level.

Communication systems based on man-machine interaction play an increasingly important role in everyday life. Whereas these systems make life easier for many people, for the hearing impaired they are indispensable. Automatic speech recognition systems in particular, thanks to the simultaneous display of text, reduce the gap separating the deaf, or the hearing impaired in general, from the world of the hearing. The paper presents an automatic aid system for the hearing impaired while behind the wheel of a car. It proposes an algorithm, based on typical speech recognition techniques, for the identification and automatic signaling on a display of emergency signals such as sirens sounded by ambulances, fire engines or any other emergency service vehicle. Tests performed show that the system guarantees an accuracy level of over 99% with a response time of less than 400 ms

Spectrum sensing is an essential function of cognitive radio technology that can enable the reuse of available radio resources by so-called secondary users without creating harmful interference with licensed users. The application of machine learning techniques to spectrum sensing has attracted considerable interest in the literature. In this contribution, we study cooperative spectrum sensing in a cognitive radio network where multiple secondary users cooperate to detect a primary user. We introduce multiple cooperative spectrum sensing schemes based on a deep neural network, which incorporate a one-dimensional convolutional neural network and a long short-term memory network. The primary objective of these schemes is to effectively learn the activity patterns of the primary user. The scenario of an imperfect transmission channel is considered for service messages to demonstrate the robustness of the proposed model. The performance of the proposed methods is evaluated with the receiver operating characteristic curve, the probability of detection for various SNR levels and the computational time. The simulation results confirm the effectiveness of the bidirectional long short-term memory-based method, surpassing the performance of the other proposed schemes and the current state-of-the-art methods in terms of detection probability, while ensuring a reasonable online detection time.