Detecting information flow direction in multivariate linear and nonlinear models

International audienceIn this paper we present an approach to analyze the direction of information flow between time series involving bidirectional relations. The intuitive idea comes from a first study dedicated to the so-called phase slope index, which is a measure originally developed to detect unidirectional relations and is based on the complex coherence function. In order to detect bidirectional flows, we propose two new causality indices supplying the previous index with two other functions, the directed coherence function and the directed transfer function. Moreover, to cope with the inability of the approaches based on coherence (ordinary or directed) or on directed transfer function to distinguish between direct and indirect relations, we propose another causality index based on the partial directed coherence to identify only direct relations. Experimental results show that some challenges have promising solutions through the use of this new indicator dealing with both linear and nonlinear multivariate models

On the evaluation of the conversational speech quality in telecommunications

International audienceIn this paper we propose an objective method to assess speech quality in the conversational context by taking into account the talking and listening speech qualities and the impact of delay. This approach is applied to the results of four subjective tests on the effects of echo, delay, packet loss and noise. The dataset is divided into training and validation sets. For the training set, a multiple linear regression is applied to determine a relationship between conversational, talking and listening speech qualities and the delay value. The multiple linear regression leads to an accurate estimation of the conversational scores with high correlation and low error between subjective and estimated scores, both on the training and validation sets. In addition, a validation is performed on the data of a subjective test found in the literature which confirms the reliability of the regression. The relationship is then applied to an objective level by replacing talking and listening subjective scores with talking and listening objective scores provided by existing objective models, fed by speech signals recorded during the subjective tests. The conversational model achieves high perfor- mance as revealed by comparison with the test results and with the existing standard methodology “E-model”, presented in the ITU-T (International Telecommunication Union) Recommendation G.107

Characterizing Peaks in Acceleration Signals-Application to Physical Activity Detection Using Wearable Sensors

International audienceThe world is getting older by the minute due to rising life expectancy, leading to an urgent need for the continuous monitoring of patients. Tracking human activities without hospitalization has been tackled in the past decade thanks to the advancement of sensing technologies and wearable devices. However, the limited power resources of microcontrollers and the power consumption due to the use of different sensors are two issues that make the recognition process via embedded systems an open research topic to date. Consequently, this paper proposes a low-cost machine learning-based human activity recognition algorithm. It detects cyclic activities from wrist-worn tri-axial accelerometer data and classifies them into four classes. Specifically, a novel and smart peak detection technique is proposed, followed by the extraction of small handcrafted feature vectors, representing the input of a novel machine-learning architecture. These three contributions are approved by experimental results on a 3300-file dataset, showing an accuracy of 99.21% with a low computational cost thanks to an efficient implementation for feature extraction. The effectiveness of the proposed recognition process is validated in real world conditions

Wellness sensors and proprietary protocols, a solution for health monitoring?

International audienceTelemedicine and telehomecare allow elders to keep living in their own home in better conditions and without intrusion of privacy. To better monitor elders' health, we propose to develop an automated system that measures different biomedical and environmental parameters. In this context, we first compare standard and proprietary protocols on the one hand and wellness and medical devices on the other hand. Then we deploy our application able to retrieve measurements values from different types of sensors using different protocols

On the identification of relevant degradation indicators in super wideband listening quality assessment models

International audienceRecently, new objective speech quality evaluation methods, designed and adapted to new high voice quality contexts, have been developed. One interest of these methods is that they integrate voice quality perceptual dimensions reflecting the effects of frequency-response distortions, discontinuities, noise and/or speech level deviations respectively. This makes it possible to use these methods also to provide diagnostic information about specific aspects of the transmission systems' quality, as perceived by end-users. In this paper, we present and analyze in depth two of these approaches namely POLQA (Perceived Objective Listening Quality Assessment) and DIAL (Diagnostic Instrumental Assessment of Listening quality), in terms of quality degradation indicators related to the perceptual dimensions these models could embed. The main goal of our work is to find and propose the most robust quality degradation indicators to reliably characterize the impact of degradations relative to the perceptual dimensions described above and to identify the underlying technical causes in super wideband telephone communications [50, 14000] Hz. To do so, the first step of our study was to identify in both models the correspondence between perceptual dimensions and quality degradation indicators. Such indicators could be either present in the model itself or derived from our own investigation of the model. In a second step, we analyzed the performance and robustness of the identified quality degradation indicators on speech samples only impaired by one degradation (representative of one perceptual dimension) at a time. This study highlighted the reliability of some of the quality degradation indicators embedded in the two models under study and stood for a first step in the evaluation of performance of these indicators to quantify the degradation for which they were designed

Improving the dynamics of responses to amplitude modulated stimuli by modeling inhibitory interneurons in cochlear nucleus.

International audienceAmplitude modulation is an important feature of communication sounds. A phenomenological model of the auditory pathway that reproduces amplitude modulation coding from the outer ear to the inferior colliculus is presented. It is based on Hewitt and Meddis' work. To improve the temporal coding for high level stimuli, high spontaneous rate and low spontaneous rate auditory nerve fibers innervate chopper cells of the cochlear nucleus. Wideband inhibitory interneurons which limit high spontaneous rate fibers connected to chopper units are added in this nucleus. The realistic structure we propose gives results closer to physiological data in terms of synchronization

Discriminatory validity of dyslexia screening tasks in French school age children.

International audienceDyslexia is a specific disorder of language. Researches led on dyslexia origin have conducted to multiple hypotheses and various rehabilitation treatments. In this context, practitioners can be interested in using an automatic tool to help in diagnosing dyslexia. This tool should evaluate children's own deficit and advise adapted rehabilitation. This paper presents the conception of a preliminary test containing the most representative dyslexia evaluation tasks from literature and the first results concerning the discriminatory validity of this preliminary test in French school age children (8-10 years). Moreover a selection of significant tasks to optimize the detection of dyslexia is proposed. These tasks will build up the first step of the automatic tool

On the Use of Kernel Fisher Discriminant Analysis as a Reduction Method for the Classification of EMG Signals

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An Optimized Dictionary-Based Model Identification Method in the Scope of Brain Effective Connectivity

International audienceIn the context of epilepsy, understanding the interactions among different brain regions during epileptic seizures is often crucial to identify brain regions involved in the seizure onset. In this letter, we propose a dictionary-based method to detect and reconstruct the relationships between brain regions during epileptic seizures based on electroencephalographic signals. To this end, a proximal alternating linearized minimization algorithm is used to quantify the relations between each of the dictionary candidates. Then, by introducing an adaptive threshold, an estimation of the involvement of the different brain regions in the seizure onset is performed, which proves to be consistent with the clinical expertise