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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]

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]