Recognition process of jamming signals superimposed on GSM-R radiocommunications

This paper explores an approach trying to recognize the presence of electromagnetic attacks on an equipment. Wireless communications are widely used in railway traffic management systems. Such systems are probably susceptible to be disturbed by malicious actions involving jammers. The general objective of this work is to develop a specific method enabling to detect and to recognize these types of interfering signals. This method could be used to involve adequate reactions in order to reduce the impact on the railway network. This paper focuses on the recognition method. It is based on accurate statistical models of signals generated by jammers. This work is carried out in the framework of the European project “SECRET” for SECurity of Railways against Electromagnetic aTtacks

Use of Bimodal Coherence to Resolve Spectral Indeterminacy in Convolutive BSS

Recent studies show that visual information contained in visual speech can be helpful for the performance enhancement of audio-only blind source separation (BSS) algorithms. Such information is exploited through the statistical characterisation of the coherence between the audio and visual speech using, e.g. a Gaussian mixture model (GMM). In this paper, we present two new contributions. An adapted expectation maximization (AEM) algorithm is proposed in the training process to model the audio-visual coherence upon the extracted features. The coherence is exploited to solve the permutation problem in the frequency domain using a new sorting scheme. We test our algorithm on the XM2VTS multimodal database. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS

The Natural Statistics of Audiovisual Speech

Humans, like other animals, are exposed to a continuous stream of signals, which are dynamic, multimodal, extended, and time varying in nature. This complex input space must be transduced and sampled by our sensory systems and transmitted to the brain where it can guide the selection of appropriate actions. To simplify this process, it's been suggested that the brain exploits statistical regularities in the stimulus space. Tests of this idea have largely been confined to unimodal signals and natural scenes. One important class of multisensory signals for which a quantitative input space characterization is unavailable is human speech. We do not understand what signals our brain has to actively piece together from an audiovisual speech stream to arrive at a percept versus what is already embedded in the signal structure of the stream itself. In essence, we do not have a clear understanding of the natural statistics of audiovisual speech. In the present study, we identified the following major statistical features of audiovisual speech. First, we observed robust correlations and close temporal correspondence between the area of the mouth opening and the acoustic envelope. Second, we found the strongest correlation between the area of the mouth opening and vocal tract resonances. Third, we observed that both area of the mouth opening and the voice envelope are temporally modulated in the 2–7 Hz frequency range. Finally, we show that the timing of mouth movements relative to the onset of the voice is consistently between 100 and 300 ms. We interpret these data in the context of recent neural theories of speech which suggest that speech communication is a reciprocally coupled, multisensory event, whereby the outputs of the signaler are matched to the neural processes of the receiver

Multimodal speech separation

The work of Bernstein and Benoît has confirmed that it is advantageous to use multiple senses, for example to employ both audio and visual modalities, in speech perception. As a consequence, looking at the speaker's face can be useful to better hear a speech signal in a noisy environment and to extract it from competing sources, as originally identified by Cherry, who posed the so-called "Cocktail Party" problem. To exploit the intrinsic coherence between audition and vision within a machine, the method of blind source separation (BSS) is particularly attractive. © 2010 Springer-Verlag

Blind Non-stationnary Sources Separation by Sparsity in a Linear Instantaneous Mixture

International audienceIn the case of a determined linear instantaneous mixture, a method to estimate non-stationnary sources with non activity periods is proposed. The method is based on the assumption that speech signals are inactive in some unknown temporal periods. Such silence periods allow to estimate the rows of the demixing matrix by a new algorithm called Direction Estimation of Separating Matrix (DESM). The periods of sources inactivity are estimated by a generalised eigen decomposition of covariance matrices of the mixtures, and the separating matrix is then estimated by a kernel principal component analysis. Experiments are provided with determined mixtures, and shown to be efficien

Les biomolécules en biotechnologies: Chapitre 6

International audienceIntroduction aux biotechnologies en santé présente un panorama des différents types de biotechnologies devenues incontournables dans le domaine de la santé, qu’elles soient actuellement sur le marché ou en cours de développement .Après un rappel historique des évolutions et des découvertes scientifiques et un point sur l’apport prépondérant de l’informatique dans les domaines multidisciplinaires des biotechnologies, l’ouvrage aborde les différents aspects scientifiques – la production de molécules et de cellules, l’analyse, le diagnostic, la thérapeutique –, mais aussi les implications éthiques et les enjeux socio-économiques liés au développement des biotechnologies.Chaque chapitre reprend les définitions essentielles, précise le principe des technologies et laisse une place importante aux applications