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Bioimpedance spectroscopy (BIS) measurement errors may be caused by parasitic stray capacitance, impedance mismatch, cross-talking or their very likely combination. An accurate detection and identification is of extreme importance for further analysis because in some cases and for some applications, certain measurement artifacts can be corrected, minimized or even avoided. In this paper we present a robust method to detect the presence of measurement artifacts and identify what kind of measurement error is present in BIS measurements. The method is based on supervised machine learning and uses a novel set of generalist features for measurement characterization in different immittance planes. Experimental validation has been carried out using a database of complex spectra BIS measurements obtained from different BIS applications and containing six different types of errors, as well as error-free measurements. The method obtained a low classification error (0.33%) and has shown good generalization. Since both the features and the classification schema are relatively simple, the implementation of this pre-processing task in the current hardware of bioimpedance spectrometers is possible. [ABSTRACT FROM AUTHOR]

Speech acquisition using microphone arrays is included in a variety of trending applications. Multichannel speech enhancement based on spatial filtering aims at improving the quality of the acquired speech. The optimization of the filter coefficients has been the primary focus in beamformer design. However, the array configuration plays an important role in the quality of the speech acquisition system and it should also be optimized. In some applications, the possibilities for microphone placement are very large, and the search of the optimum solution, which involves exploring all possible microphone configurations, is an unfeasible task. This work presents a novel search algorithm based on evolutionary computation to approximate the optimum array configuration. A realistic car noise model based on real measurements is proposed and used in the design. The obtained results support the suitability of the method, notably improving the results obtained by linear arrays with the same number of elements, which are the typical arrays currently assembled in vehicles. [ABSTRACT FROM AUTHOR]

Abstract: The improvement of speech intelligibility in hearing aids is still an unsolved problem. Modern devices, either monaural or binaural, may include microphone arrays to provide directivity by means of spatial filtering, but the signals that arrive at the array are distorted by the so-called head shadow effect, which must be considered in the design. The fact that these effects are highly dependent on a person causes the design of an array customized for a subject to need a correct measurement of these effects, which is not practical in real scenarios. The lack of information about the head causes directivity reduction and distortions. In this work we propose three different methods to compute optimized filter coefficients with the purpose of maximizing the array gain and minimizing distortions, and with the objective that these filter coefficients are valid for all users. Furthermore, measurements of the amplitude and phase distortions caused by a lack of head information in the design are introduced. In addition, the methods proposed here have been evaluated in 13 different array configurations. Results show that these approaches reduce significantly the gain reduction and distortions, obtaining a similar intelligibility in comparison to the case where head information is available. [Copyright &y& Elsevier]

Abstract: Speech source separation in the time–frequency domain is a modern approach that exploits the sparsity of speech when it is represented in such domain. Several methods based on this approach exist, DUET being the most remarkable of these. In this work we propose a novel time–frequency domain algorithm for sound source separation, based on a generalization of the mean shift clustering method. The proposed algorithm can be applied to separate an undetermined number of sources from two mixtures. The new method is compared to the DUET algorithm, as well as with a modification of DUET based on k-means, for different types of mixtures: linear speech mixtures, binaural speech mixtures, linear speech and noise mixtures and linear speech and music mixtures. From the results we note that the use of the proposed algorithm based on mean shift for speech separation shows a significantly better performance than the DUET algorithm. [Copyright &y& Elsevier]

Desynchronization degrades the performance of many signal processing algorithms in Wireless Acoustic Sensor Networks. It is mainly caused by the different distances between the source and each node and by the clock phase offset and frequency skew. Classical solutions use clock synchronization protocols and algorithms in the communication layer, but these alternatives do not tackle the lack of synchronization caused by the distances between sources and nodes. In this paper, we present a novel study of the synchronization problem in acoustic sensor networks from a signal processing point of view. First, we propose a theoretical framework that allows us to study the effects of misalignment over any short-time based algorithm, focusing on the requirements of the effective length of the analysis time frame. From this framework, a theoretical synchronization delay is established aimed at reducing the required length of the time frame. Second, two novel alignment methods are developed and are tuned up to reduce the amount of synchronization information required for transmission. The results obtained demonstrate that our proposed methods represent a good solution in terms of performance over the quality of a standard Blind Source Separation algorithm, allowing us to reduce the transmission bandwidth required for synchronization data. [ABSTRACT FROM AUTHOR]

The Spanish Ministry of Defense, through its Future Combatant program, has sought to develop technology aids with the aim of extending combatants' operational capabilities. Within this framework the ATREC project funded by the "Coincidente" program aims at analyzing diverse biometrics to assess by real time monitoring the stress levels of combatants. This project combines multidisciplinary disciplines and fields, including wearable instrumentation, textile technology, signal processing, pattern recognition and psychological analysis of the obtained information. In this work the ATREC project is described, including the different execution phases, the wearable biomedical measurement systems, the experimental setup, the biomedical signal analysis and speech processing performed. The preliminary results obtained from the data analysis collected during the first phase of the project are presented, indicating the good classification performance exhibited when using features obtained from electrocardiographic recordings and electrical bioimpedance measurements from the thorax. These results suggest that cardiac and respiration activity offer better biomarkers for assessment of stress than speech, galvanic skin response or skin temperature when recorded with wearable biomedical measurement systems. [ABSTRACT FROM AUTHOR]

Advances in textile materials, technology and miniaturization of electronics for measurement instrumentation has boosted the development of wearable measurement systems. In several projects sensorized garments and non-invasive instrumentation have been integrated to assess on emotional, cognitive responses as well as physical arousal and status of mental stress through the study of the autonomous nervous system. Assessing the mental state of workers under stressful conditions is critical to identify which workers are in the proper state of mind and which are not ready to undertake a mission, which might consequently risk their own life and the lives of others. The project Assessment in Real Time of the Stress in Combatants (ATREC) aims to enable real time assessment of mental stress of the Spanish Armed Forces during military activities using a wearable measurement system containing sensorized garments and textile-enabled non-invasive instrumentation. This work describes the multiparametric sensorized garments and measurement instrumentation implemented in the first phase of the project required to evaluate physiological indicators and recording candidates that can be useful for detection of mental stress. For such purpose different sensorized garments have been constructed: a textrode chest-strap system with six repositionable textrodes, a sensorized glove and an upper-arm strap. The implemented textile-enabled instrumentation contains one skin galvanometer, two temperature sensors for skin and environmental temperature and an impedance pneumographer containing a 1-channel ECG amplifier to record cardiogenic biopotentials. With such combinations of garments and non-invasive measurement devices, a multiparametric wearable measurement system has been implemented able to record the following physiological parameters: heart and respiration rate, skin galvanic response, environmental and peripheral temperature. To ensure the proper functioning of the implemented garments and devices the full series of 12 sets have been functionally tested recording cardiogenic biopotential, thoracic impedance, galvanic skin response and temperature values. The experimental results indicate that the implemented wearable measurement systems operate according to the specifications and are ready to be used for mental stress experiments, which will be executed in the coming phases of the project with dozens of healthy volunteers. [ABSTRACT FROM AUTHOR]