FPGA Implementation of an Adaptive Noise Canceller for Robust Speech Enhancement Interfaces

This paper describes the design and implementation results of an adaptive Noise Canceller useful for the construction of Robust Speech Enhancement Interfaces. The algorithm being used has very good performance for real time applications. Its main disadvantage is the requirement of calculating several operations of division, having a high computational cost. Besides that, the accuracy of the algorithm is critical in fixed-point representation due to the wide range of the upper and lower bounds of the variables implied in the algorithm. To solve this problem, the accuracy is studied and according to the results obtained a specific word-length has been adopted for each variable. The algorithm has been implemented for Altera and Xilinx FPGAs using high level synthesis tools. The results for a fixed format of 40 bits for all the variables and for a specific word-length for each variable are analyzed and discussed

A hybrid unsupervised and supervised clustering applied to microarray data

This work shows how one can determine an optimal combination of clustering algorithms by performing a hybrid biclustering of data with unsupervised methods, and how to extract coherent and typically small clusters of genes that vary as much as possible across the samples using an supervised method like Gene Shaving

Robust and Complex Approach of Pathological Speech Signal Analysis

This article presents a~study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a~special focus on parametrization techniques. It provides a~description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to $100.0\pm0.0\,\%$) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a~length of sustained vowels, however in the case of Pr{\'i}ncipe de Asturias (PdA) Hospital in Alcal{\'a} de Henares of Madrid database we made improvements in classification accuracy ($82.1\pm3.3\,\%$) and specificity ($83.8\pm5.1\,\%$) when considering a~single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney~U test as significant (p < 0.05) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a~cepstral peak prominence extracted from the first intrinsic mode function ($p = 6.9443\cdot10^{-32}$) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The article also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view

Robust and Complex Approach of Pathological Speech Signal Analysis

This article presents a~study of the approaches in the state-of-the-art in the field of pathological speech signal analysis with a~special focus on parametrization techniques. It provides a~description of 92 speech features where some of them are already widely used in this field of science and some of them have not been tried yet (they come from different areas of speech signal processing like speech recognition or coding). As an original contribution, this work introduces 36 completely new pathological voice measures based on modulation spectra, inferior colliculus coefficients, bicepstrum, sample and approximate entropy and empirical mode decomposition. The significance of these features was tested on 3 (English, Spanish and Czech) pathological voice databases with respect to classification accuracy, sensitivity and specificity. To our best knowledge the introduced approach based on complex feature extraction and robust testing outperformed all works that have been published already in this field. The results (accuracy, sensitivity and specificity equal to $100.0\pm0.0\,\%$) are discussable in the case of Massachusetts Eye and Ear Infirmary (MEEI) database because of its limitation related to a~length of sustained vowels, however in the case of Pr{\'i}ncipe de Asturias (PdA) Hospital in Alcal{\'a} de Henares of Madrid database we made improvements in classification accuracy ($82.1\pm3.3\,\%$) and specificity ($83.8\pm5.1\,\%$) when considering a~single-classifier approach. Hopefully, large improvements may be achieved in the case of Czech Parkinsonian Speech Database (PARCZ), which are discussed in this work as well. All the features introduced in this work were identified by Mann-Whitney~U test as significant (p < 0.05) when processing at least one of the mentioned databases. The largest discriminative power from these proposed features has a~cepstral peak prominence extracted from the first intrinsic mode function ($p = 6.9443\cdot10^{-32}$) which means, that among all newly designed features those that quantify especially hoarseness or breathiness are good candidates for pathological speech identification. The article also mentions some ideas for the future work in the field of pathological speech signal analysis that can be valuable especially under the clinical point of view

Post-anaesthesia pulmonary complications after use of muscle relaxants (POPULAR): a multicentre, prospective observational study

Background Results from retrospective studies suggest that use of neuromuscular blocking agents during general anaesthesia might be linked to postoperative pulmonary complications. We therefore aimed to assess whether the use of neuromuscular blocking agents is associated with postoperative pulmonary complications. Methods We did a multicentre, prospective observational cohort study. Patients were recruited from 211 hospitals in 28 European countries. We included patients (aged ≥18 years) who received general anaesthesia for any in-hospital procedure except cardiac surgery. Patient characteristics, surgical and anaesthetic details, and chart review at discharge were prospectively collected over 2 weeks. Additionally, each patient underwent postoperative physical examination within 3 days of surgery to check for adverse pulmonary events. The study outcome was the incidence of postoperative pulmonary complications from the end of surgery up to postoperative day 28. Logistic regression analyses were adjusted for surgical factors and patients’ preoperative physical status, providing adjusted odds ratios (ORadj) and adjusted absolute risk reduction (ARRadj). This study is registered with ClinicalTrials.gov, number NCT01865513. Findings Between June 16, 2014, and April 29, 2015, data from 22803 patients were collected. The use of neuromuscular blocking agents was associated with an increased incidence of postoperative pulmonary complications in patients who had undergone general anaesthesia (1658 [7·6%] of 21694); ORadj 1·86, 95% CI 1·53–2·26; ARRadj –4·4%, 95% CI –5·5 to –3·2). Only 2·3% of high-risk surgical patients and those with adverse respiratory profiles were anaesthetised without neuromuscular blocking agents. The use of neuromuscular monitoring (ORadj 1·31, 95% CI 1·15–1·49; ARRadj –2·6%, 95% CI –3·9 to –1·4) and the administration of reversal agents (1·23, 1·07–1·41; –1·9%, –3·2 to –0·7) were not associated with a decreased risk of postoperative pulmonary complications. Neither the choice of sugammadex instead of neostigmine for reversal (ORadj 1·03, 95% CI 0·85–1·25; ARRadj –0·3%, 95% CI –2·4 to 1·5) nor extubation at a train-of-four ratio of 0·9 or more (1·03, 0·82–1·31; –0·4%, –3·5 to 2·2) was associated with better pulmonary outcomes. Interpretation We showed that the use of neuromuscular blocking drugs in general anaesthesia is associated with an increased risk of postoperative pulmonary complications. Anaesthetists must balance the potential benefits of neuromuscular blockade against the increased risk of postoperative pulmonary complications