Dominique Pastor

Dominique Pastor
Institut Mines-Télécom | telecom-sudparis.eu · Mathematical & Electronical Engineering Dept.

Professor at IMT Atlantique
Robust and nonparametric statistical signal processing; machine learning; mathematical models of resilience

About

135
Publications
13,891
Reads
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953
Citations
Introduction
My research interests are threefold. First, I'm still working on the Random Distortion Testing (RDT) framework in Statistical Signal Processing I introduced a few years ago. Second, I'm working on new mathematical results and algorithms for distributed clustering over a network of sensors. Third, my newest focus is on mathematical models of resilience along an interdisciplinary approach at the interface of statistical signal processing, category theory and biology.
Additional affiliations
September 2002 - September 2021
IMT Atlantique
Position
  • Professor (Full)
Description
  • I am a mathematical engineer, endeavouring to solve practical problems by possibly new theories with deep impact beyond the original issue. I am an expert on statistical signal processing and sparse transforms. Regarding teaching activities, considering that education and research are indivisible, I strive to give students the opportunity to keep in touch with the most advanced achievements in my domain and to hone their critical sense by confronting usual concepts to new ones.
June 2000 - August 2002
Altran Technologies Nederland
Position
  • Consultant
January 1998 - June 2000
Thales Nederland (formerly Signaal)
Position
  • Researcher
Description
  • Detection of small radar targets in sea cluster
Education
January 1997 - December 1997
Université de Rennes 2
Field of study
  • Signal Processing and Telecommunications
September 1983 - September 1986
Telecom bretagne
Field of study
  • Signal Processing and Telecommunications

Publications

Publications (135)
Article
Full-text available
Communicating living systems detect and process a multiplicity of events with degeneracy, to continuously cope with environmental aleatoric incertitude. The concept of holon, communicating at various scales of living organizations, is hereafter formalized through dynamical systems driven by the multiplicity of statistical models. Then, the stimulus...
Conference Paper
Full-text available
The immune system (IS) is a complex system of 10exp12 somatically diversified and communicating cells in the human body. Each cell is able to adapt its decision to collectively tolerate or reject the 10exp13 cells of our microbiota and detect other infections among the 10exp31 virus surrounding us, to insure our organism integrity. Harnessing the i...
Article
Full-text available
This paper is a cornerstone in our way to exhibit mathematical models of resilience. Perhaps, this is not really straightforward when reading the paper but I incite the reader to read Erwan Beurier's PhD downloadable from https://tel.archives-ouvertes.fr/tel-03137051
Preprint
Full-text available
The paper proposes representation functionals in a dual paradigm where learning jointly concerns both linear convolutional weights and parametric forms of nonlinear activation functions. The nonlinear forms proposed for performing the functional representation are associated with a new class of parametric neural transfer functions called rectified...
Preprint
Full-text available
In this paper, we evolve from sparsity, a key concept in robust statistics, to concepts and theoretical results of what we call the mathematics of resilience, at the interface between category theory, the theory of dynamical systems, statistical signal processing and biology. We first summarize a recent result on dynamical systems [Beurier, Pastor,...
Article
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This paper is a corrected version of the original conference paper presented at ACT 2019, with same title and authors
Preprint
Full-text available
Motivated by the concept of degeneracy in biology (Edelman, Gally 2001), we establish a first connection between the Multiplicity Principle (Ehresmann, Vanbremeersch 2007) and mathematical statistics. Specifically, we exhibit two families of statistical tests that satisfy this principle to achieve the detection of a signal in noise.
Article
Full-text available
In this paper, we propose a general framework to estimate short-time spectral amplitudes (STSA) of speech signals in noise by joint speech detection and estimation to remove or reduce background noise, without increasing signal distortion. The approach is motivated by the fact that speech signals have sparse time-frequency representations and can r...
Article
Full-text available
Automata are machines, which receive inputs, accordingly update their internal state, and produce output, and are a common abstraction for the basic building blocks used in engineering and science to describe and design complex systems. These arbitrarily simple machines can be wired together—so that the output of one is passed to another as its inp...
Article
In this work, we propose a non-parametric sequential hypothesis test based on random distortion testing (RDT). RDT addresses the problem of testing whether or not a random signal, Ξ, observed in independent and identically distributed (i.i.d) additive noise deviates by more than a specified tolerance, τ, from a fixed value, $\xi_0$ . The test is...
Article
In this work, we propose a new algorithm for sequential non-parametric hypothesis testing based on random distortion testing (RDT). The data based approach is non-parametric in the sense that the underlying signal distributions under each hypothesis are assumed to be unknown. Our previously proposed non-truncated sequential algorithm, SeqRDT, was s...
Conference Paper
Full-text available
In this paper, we propose a novel robust method for shorttime spectral amplitude (STSA) estimation in audio denoising. This method extends the smoothed sigmoid-based shrinkage (SSBS), which does not require any prior information about the probability distribution of the signal of interest. With regard to audio processing, the SSBS method yields bet...
Conference Paper
Full-text available
This paper presents a novel method for speech enhancement based on the combination of sigmoid shrinkage and bayesian estimator. The main idea is to apply a joint detection and estimation to noisy speech before using a standard minimum-mean-squared-error (MMSE) estimator. Hence, the proposed method can take advantage of two basic approaches for impr...
Article
This paper presents an automatic classification method dedicated to mysticete calls. This method relies on sparse representations which assume that mysticete calls lie in a linear subspace described by a dictionary-based representation. The classifier accounts for noise by refusing to assign the observed signal to a given class if it is not include...
Preprint
Full-text available
In sensor networks, it is not always practical to set up a fusion center. Therefore, there is need for fully decentralized clustering algorithms. Decentralized clustering algorithms should minimize the amount of data exchanged between sensors in order to reduce sensor energy consumption. In this respect, we propose one centralized and one decentral...
Article
Full-text available
We propose a novel estimator for estimating the amplitude of speech coefficients in the time-frequency domain. In order to avoid a phase spectrum estimator of complex coefficients when using the Fourier transform, we consider the discrete cosine transform (DCT). This estimator aims at minimizing the mean square error of the absolute values of the s...
Conference Paper
Random distortion testing (RDT) addresses the problem of testing whether or not a random signal deviates by more than a specified tolerance from a fixed value. The test is non-parametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed...
Article
This paper addresses the problem of testing whether, after linear transformations and possible dimensionality reductions, a random matrix of interest Θ deviates significantly from some matrix model θ0, when Θ is observed in additive independent Gaussian noise with known covariance matrix. In contrast to standard likelihood theory, the probability d...
Article
Full-text available
We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of an inferred graph structure. In more details, we introduce a three-step methodology to create convolutional layers that are adapted to the signals to process: 1) From a training set of signals, infer a graph representing the topology on whic...
Article
Full-text available
In the field of graph signal processing, defining translation operators is crucial to allow certain tasks, including moving a filter to a specific location or tracking objects. In order to successfully generalize translation-based tools existing in the time domain, graph based translations should offer multiple properties: a) the translation of a l...
Article
Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology...
Article
Full-text available
This paper considers a network of sensors without fusion center that may be difficult to set up in applications involving sensors embedded on autonomous drones or robots. In this context, this paper considers that the sensors must perform a given clustering task in a fully decentralized setup. Standard clustering algorithms usually need to know the...
Conference Paper
In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is th...
Article
Full-text available
In the field of signal processing on graphs, a key tool to process signals is the graph Fourier transform. Transforming signals into the spectral domain is accomplished using the basis of eigenvectors of the graph Laplacian matrix. Such a matrix is dependent on the topology of the graph on which the signals are defined. Therefore it is of paramount...
Article
Full-text available
Signal processing on graphs is a recent research domain that aims at generalizing classical tools in signal processing, in order to analyze signals evolving on complex domains. Such domains are represented by graphs, for which one can compute a particular matrix, called the normalized Laplacian. It was shown that the eigenvalues of this Laplacian c...
Article
Full-text available
Signal processing on graphs has received a lot of attention in the recent years. A lot of techniques have arised, inspired by classical signal processing ones, to allow studying signals on any kind of graph. A common aspect of these technique is that they require a graph correctly modeling the studied support to explain the signals that are observe...
Article
Full-text available
Nous considérons le problème d'estimer un vecteur à alphabet fini à partir d'un système sous-déterminé y = Af , où A est une matrice aléatoire générique réelle donnée de dimension n × N. Une méthode originale par optimisation convexe est proposée pour reconstruire le vecteur par minimisation 1. Cette méthode est basée sur une transformation du prob...
Conference Paper
Signal processing on graphs has received a lot of attention in the recent years. A lot of techniques have arised, inspired by classical signal processing ones, to allow studying signals on any kind of graph. A common aspect of these technique is that they require a graph correctly modeling the studied support to explain the signals that are observe...
Article
Random distortion testing (RDT) introduced in [1] is aimed at detecting any significantly big distortion of a signal with respect to a model of this signal, in presence of noise and without prior knowlegde on the distortion distribution. The RDT formulation makes it possible to state the standard change-in-mean detection problem differently. It lea...
Conference Paper
Full-text available
Maximum-Likelihood (ML) joint detection has been proposed as an optimal strategy that detects simultaneously the transmitted signals. In very large multiple-input-multiple output (MIMO) systems, the ML detector becomes intractable due the computational cost that increases exponentially with the antenna dimensions. In this paper, we propose a relaxe...
Conference Paper
In various situations surrounding noise can be disturbing or stressful. Active Noise Control (ANC) is an effective technology for noise reduction that works as a good complement of the passive isolation offered by traditional headphones. Digital ANC systems are progressively finding their way into consumer electronic applications, in replacement of...
Data
Complete sourcecode in MatLab/Octave to run the experiments described in the paper "Using Tags to Improve Diversity of Sparse Associative Memories". Licensed under CRAPL (opensource).
Data
Full-text available
Presentation done at the IARIA COGNITIVE 2015 by Stephen Larroque on paper "Using Tags to Improve Diversity of Sparse Associative Memories". This is a hands-on presentation to give an intuitive understanding of the proposed mechanism to increase the storage capacity of associative memories by using colored graphs. More theoretical content and an ex...
Article
Full-text available
We propose a novel method for noise power spectrum estimation in speech enhancement. This method called extended-DATE (E-DATE) extends the $d$ -dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation in “Robust estimation of noise standard deviation in presence of signals wit...
Article
We consider the problem of estimating a deterministic finite alphabet vector $ { f}$ from underdetermined measurements $ { y}= { A} { f} $ , where $ { A}$ is a given (random) $n times N$ matrix. Two new convex optimization methods are introduced for the recovery of finite alphabet signals via $ell _{1}$ -norm minimization. The first method is based...
Conference Paper
Full-text available
Associative memories, a classical model for brain long-term memory, face interferences between old and new memories. Usually, the only remedy is to enlarge the network so as to retain more memories without collisions: this is the network's size--diversity trade-off. We propose a novel way of representing data in these networks to provide another me...
Article
Full-text available
We consider the problem of testing whether the energy of a random signal projected onto a known subspace exceeds some specified value $tau geq 0$. The probability distribution of the signal is assumed to be unknown and this signal is observed in additive and independent white Gaussian noise with known variance. The proposed theoretical framework re...
Article
Full-text available
ó Consider the wavelet packet coefcients issued from the decomposition of a random process stationary in the wide- sense. We address the asymptotic behaviour of the autocorre- lation of these wavelet packet coefcients. In a rst step, we explain why this analysis is more intricate than that already achieved by several authors in the case of the stan...
Patent
(en) [origin: WO2013139979A1] The invention relates to a method for detecting the presence of an anomaly Delta(t) comprised within an observed physical signal Y(t), said observed signal comprising an addition of a physical disturbance signal X(t), and a reference signal f(t), and said anomaly being relative to a change in the behaviour of the refer...
Conference Paper
Full-text available
In this paper we address the problem of large dimension decoding in MIMO systems. The complexity of the optimal maximum likelihood detection makes it unfeasible in practice when the number of antennas, the channel impulse response length or the source constellation size become too high. We consider a MIMO system with finite constellation and model...
Article
This paper addresses the problem of testing whether the Mahalanobis distance between a random signal Θ and a known deterministic model θ0 exceeds some given non-negative real number or not, when Θ has unknown probability distribution and is observed in additive independent Gaussian noise with positive definite covariance matrix. When Θ is determini...
Article
Automatic monitoring of mechanical ventilation system becomes more and more important with respect to the number of patients per clinician. In this paper, the automatic detections of dynamic hyperinflation (PEEPi) and asynchrony in a monitoring framework are considered. The proposed detection methods are based on a robust non-parametric hypothesis...
Conference Paper
We address Random Distortion Testing (RDT), that is, the problem of testing whether the Mahalanobis distance between a random signal Θ and a known deterministic model θ0 exceeds some given τ ≥ 0 or not, when Θ has unknown probability distribution and is observed in additive independent Gaussian noise with positive definite covariance matrix. A suit...
Article
Statistical properties of real-valued symmetric -stable noise after short-time Fourier transformation are derived. Circularity, stationarity and dependence between the real and imaginary components are studied as a function of the STFT parameters and the stability index .
Conference Paper
Full-text available
This paper addresses the underdetermined source separation problem of finite alphabet signals. We present a new framework for recovering finite alphabet signals. We formulate this problem as a recovery of sparse signals from highly incomplete measurements. It is known that sparse solutions can be obtained by ℓ1 minimization, through convex optimiza...
Article
Full-text available
We address the problem of blind source separation in the underdetermined mixture case. Two statistical tests are proposed to reduce the number of empirical parameters involved in standard sparseness-based underdetermined blind source separation (UBSS) methods. The first test performs multisource selection of the suitable time-frequency points for s...
Article
Full-text available
Background Dynamic hyperinflation, hereafter called AutoPEEP (auto-positive end expiratory pressure) with some slight language abuse, is a frequent deleterious phenomenon in patients undergoing mechanical ventilation. Although not readily quantifiable, AutoPEEP can be recognized on the expiratory portion of the flow waveform. If expiratory flow doe...
Article
Full-text available
In many applications, $d$ -dimensional observations result from the random presence or absence of random signals in independent and additive white Gaussian noise. An estimate of the noise standard deviation can then be very useful to detect or to estimate these signals, especially when standard likelihood theory cannot be applied because of too lit...
Conference Paper
Full-text available
This paper introduces a method to automatically detect AutoPEEP (pulmonary distension), a frequent asynchrony in the patient-ventilator interface. The detection algorithm is developed based on a robust non-parametric hypothesis testing that requires no prior information on the distribution of the signal. The experiment results have shown that the p...
Article
We propose a novel framework for noise robust automatic speech recognition (ASR) based on cochlear implant-like spectrally reduced speech (SRS). Two experimental protocols (EPs) are proposed in order to clarify the advantage of using SRS for noise robust ASR. These two EPs assess the SRS in both the training and testing environments. Speech enhance...
Article
Full-text available
Finding good descriptors, capable of discriminating hotspot residues from others, is still a challenge in many attempts to understand protein interaction. In this paper, descriptors issued from the analysis of amino acid sequences using digital signal processing (DSP) techniques are shown to be as good as those derived from protein tertiary structu...
Conference Paper
Full-text available
We address the problem of blind source separation in the underdetermined and instantaneous mixture case. The proposed method is based on an algorithm developed by Aissa-El-Bey and al.. This algorithm requires a good choice of the noise threshold and does not take into account the noise contribution in the inversion process. In order to overcome the...