Représentation et reconnaissance des signaux acoustiques sous-marins

This thesis aims to identify and develop new representation methods of the underwater acoustic signals. Ourgoal is to interpret, recognize and automatically identify underwater signals from sonar system. The idea hereis not to replace the machine petty officer, whose experience and hearing finesse make it indispensable for thisposition, but to automate certain processing information to relieve the analyst and offer support to the decision.In this thesis, we are inspired by what is best in this area: the human. On board a submarine, they are experts inthe analysis of sounds that are entrusted to the listening task signals to identify suspicious sounds. Whatinterests us is the ability of the human to determine the class of a sound signal on the basis of his hearing.Indeed, the human ear has the power to differentiate two distinct sounds through psychoacoustic perceptualcriteria such as tone, pitch, intensity. The operator is also helped by representations of the sound signal in thetime-frequency plane coming displayed on the workstation. So we designed a representation that approximatesthe physiology of the human ear, i.e how humans hear and perceive frequencies. To construct thisrepresentation space, we will use an algorithm that we called the Hearingogram and a denoised version theDenoised Hearingoram. All these representations will input an automatic identification system, which wasdesigned during this thesis and is based on the use of SVM.Cette thèse a pour but de définir et concevoir de nouvelles techniques de représentation des signauxacoustiques sous-marins. Notre objectif est d’interpréter, reconnaître et identifier de façon automatique lessignaux sous-marins émanant du système sonar. L’idée ici n’est pas de substituer la machine à l’officiermarinier, dont l’expérience et la finesse d’ouïe le rendent indispensable à ce poste, mais d’automatiser certainstraitements de l’information pour soulager l’analyste et lui offrir une aide à la décision.Dans cette thèse, nous nous inspirons de ce qui se fait de mieux dans ce domaine : l’humain. A bord d’un sousmarin,ce sont des experts de l’analyse des sons à qui l’on confie la tâche d'écoute des signaux afin de repérerles sons suspects. Ce qui nous intéresse, c’est cette capacité de l’humain à déterminer la classe d’un signalsonore sur la base de son acuité auditive. En effet, l’oreille humaine a le pouvoir de différencier deux sonsdistincts à travers des critères perceptuels psycho-acoustiques tels que le timbre, la hauteur, l’intensité.L’opérateur est également aidé par des représentations du signal sonore dans le plan temps-fréquence quiviennent s’afficher sur son poste de travail. Ainsi nous avons conçu une représentation qui se rapproche de laphysiologie de l’oreille humaine, autrement dit de la façon dont l’homme entend et perçoit les fréquences. Pourconstruire cet espace de représentation, nous utiliserons un algorithme que nous avons appelé l’Hearingogramet sa version débruitée le Denoised Hearingoram. Toutes ces représentations seront en entrée d’un systèmed’identification automatique, qui a été conçu durant cette thèse et qui est basé sur l’utilisation des SVM

ENABLING VIRTUAL ACOUSTIC BACKGROUND FOR VIDEO AND AUDIO CONFERENCING

Virtual background is an emerging feature of collaboration services such as conference calling applications and platforms. It enables users to choose a picture or video as their background to avoid distractions and protect privacy. In this article, we propose to enable virtual acoustic background for video/audio conferencing system. Such a feature can improve speech clarity and intelligibility for conference participants by making the collaboration more efficient and professional

Design of a Time-Frequency Algorithm for Automatic Eeg Artifact Removal

The injuries suffered by newborns during birth are a major health issue. To improve the health outcomes of sick newborns using EEG measurements, a number of recent studies focused on the use of high-resolution Time-Frequency Distributions to extract critical information from the collected signals [1]. Several algorithms have been proposed. A major problem in the implementation of such algorithms for fully automated EEG signal classification systems is caused by artifacts. In particular, previous studies have shown that a respiratory artifact looks like a seizure signal and can be misinterpreted by the automatic abnormality detection system thus resulting in false alarms. Hence, the successful removal of the artifacts is important, as shown in several previous studies [2]; and, there are two basic approaches for this: (1) use machine learning technique to detect and reject EEG segments corrupted by artifact; but this would result in the loss of EEG data [2]. (2) Correct EEG segments corrupted by artifacts; some artifacts can be corrected by a simple filter in a frequency domain, e.g. notch filter can be used to remove 50 Hz noise. This approach does not require any reference signals. For more complicated cases, when the spectrum of artifacts overlaps with the spectrum of EEG signals, blind source separation (BSS) algorithms can be used. Typically a multi-component EEG signal is transformed into a linear combination of independent components (that can be interpreted as channels (ICs)) by blind source separation techniques such as the independent component analysis (ICA) or canonical correlation analysis. The independent channels that are corrupted by artifacts are identified either manually or automatically using correlation information from a reference signal. The artifact free signal is then constructed by combining only artifact-free ICs.qscienc

Representation and recognition of underwater acoustic signals

Cette thèse a pour but de définir et concevoir de nouvelles techniques de représentation des signauxacoustiques sous-marins. Notre objectif est d’interpréter, reconnaître et identifier de façon automatique lessignaux sous-marins émanant du système sonar. L’idée ici n’est pas de substituer la machine à l’officiermarinier, dont l’expérience et la finesse d’ouïe le rendent indispensable à ce poste, mais d’automatiser certainstraitements de l’information pour soulager l’analyste et lui offrir une aide à la décision.Dans cette thèse, nous nous inspirons de ce qui se fait de mieux dans ce domaine : l’humain. A bord d’un sousmarin,ce sont des experts de l’analyse des sons à qui l’on confie la tâche d'écoute des signaux afin de repérerles sons suspects. Ce qui nous intéresse, c’est cette capacité de l’humain à déterminer la classe d’un signalsonore sur la base de son acuité auditive. En effet, l’oreille humaine a le pouvoir de différencier deux sonsdistincts à travers des critères perceptuels psycho-acoustiques tels que le timbre, la hauteur, l’intensité.L’opérateur est également aidé par des représentations du signal sonore dans le plan temps-fréquence quiviennent s’afficher sur son poste de travail. Ainsi nous avons conçu une représentation qui se rapproche de laphysiologie de l’oreille humaine, autrement dit de la façon dont l’homme entend et perçoit les fréquences. Pourconstruire cet espace de représentation, nous utiliserons un algorithme que nous avons appelé l’Hearingogramet sa version débruitée le Denoised Hearingoram. Toutes ces représentations seront en entrée d’un systèmed’identification automatique, qui a été conçu durant cette thèse et qui est basé sur l’utilisation des SVM.This thesis aims to identify and develop new representation methods of the underwater acoustic signals. Ourgoal is to interpret, recognize and automatically identify underwater signals from sonar system. The idea hereis not to replace the machine petty officer, whose experience and hearing finesse make it indispensable for thisposition, but to automate certain processing information to relieve the analyst and offer support to the decision.In this thesis, we are inspired by what is best in this area: the human. On board a submarine, they are experts inthe analysis of sounds that are entrusted to the listening task signals to identify suspicious sounds. Whatinterests us is the ability of the human to determine the class of a sound signal on the basis of his hearing.Indeed, the human ear has the power to differentiate two distinct sounds through psychoacoustic perceptualcriteria such as tone, pitch, intensity. The operator is also helped by representations of the sound signal in thetime-frequency plane coming displayed on the workstation. So we designed a representation that approximatesthe physiology of the human ear, i.e how humans hear and perceive frequencies. To construct thisrepresentation space, we will use an algorithm that we called the Hearingogram and a denoised version theDenoised Hearingoram. All these representations will input an automatic identification system, which wasdesigned during this thesis and is based on the use of SVM

Efficient software platform TFSAP 7.1 and Matlab package to compute Time-Frequency Distributions and related Time-Scale methods with extraction of signal characteristics

This article describes the source code used in the TFSAP toolbox (Boashash, 2016). It is extended with additional functions to allow reproducible research as presented in Boashash and Ouelha (in press). These codes can be used for analysis and classification to (1) generate Time-Frequencydistributions (TFDs) or Time-Scale distributions (TSDs), (2) extract efficient features for change detection and (3) select the most relevant features using different techniques (Boashash and Ouelha, 2016). The code can be downloaded from: https://github.com/ElsevierSoftwareX/SOFTX-D-17-00059

An improved design of high-resolution quadratic time-frequency distributions for the analysis of nonstationary multicomponent signals using directional compact kernels

This paper presents a new advanced methodology for designing high resolution time-frequency distributions (TFDs) of multicomponent nonstationary signals that can be approximated using piece-wise linear frequency modulated (PW-LFM) signals. Most previous kernel design methods assumed that signals auto-Terms are mostly centered around the origin of the nu ambiguity domain while signal cross-Terms are mostly away from the origin. This study uses a multicomponent test signal for which each component is modeled as a PW-LFM signal; it finds that the above assumption is a very rough approximation of the location of the auto-Terms energy and cross-Terms energy in the ambiguity domain and it is only valid for signals that are well separated in the (t,f) domain. A refined investigation led to improved specifications for separating cross-Terms from auto-Terms in the nu ambiguity domain. The resulting approach first represents the signal in the ambiguity domain, and then applies a multidirectional signal dependent compact kernel that accounts for the direction of the auto-Terms energy. The resulting multidirectional distribution (MDD) approach proves to be more effective than classical methods like extended modified B distribution, S-method, or compact kernel distribution in terms of auto-Terms resolution and cross-Terms suppression. Results on simulated and real data validate the improved performance of the MDD, showing up to 8% gain as compared to more standard state-of-The-Art TFDs

An efficient inverse short-time Fourier transform algorithm for improved signal reconstruction by time-frequency synthesis: Optimality and computational issues

This paper presents an improved signal reconstruction method based on a new inverse short-time Fourier transform (ISTFT) estimator. The main challenge addressed in this study is to design a more computationally efficient algorithm called exact formal approach (EFA) which overcomes the drawbacks of the popular overlap and add (OLA) and least squares (LS) methods in several cases of practical interest. The proposed EFA algorithm is based on a vector formulation and the exploitation of properties of the matrix formed by signal samples and frames corresponding to signals segments with overlap. A detailed comparative study shows the advantages of the EFA compared to the OLA and LS methods. Several experiments illustrate the performance and properties of the different estimators. The criteria of comparison are based on synthesis quality and denoising efficiency. The results indicate that, (1) from a computational point of view, the proposed algorithm EFA outperforms other popular ISTFT algorithms including OLA and LS and (2) that the EFA and LS have similar results in terms of synthesis quality and both outperform the algorithm currently most used for ISTFT, the OLA. The proposed EFA estimator can then improve ISTFT based applications involving signal enhancement and denoising.The third author Prof. Boashash wishes to acknowledge receiving funding from QNRF grant NPRP 6-885-2-364. This paper relates to Aim 6 of this NPRP project.Scopu

Performance evaluation of time-frequency image feature sets for improved classification and analysis of non-stationary signals: Application to newborn EEG seizure detection

This study demonstrates that a time-frequency (TF) image pattern recognition approach offers significant advantages over standard signal classification methods that use t-domain only or f-domain only features. Two approaches are considered and compared. The paper describes the significance of the standard TF approach for non-stationary signals; TF signal (TFS) features are defined by extending t-domain or f-domain features to a joint (t, f) domain resulting in e.g. TF flatness and TF flux. The performance of the extended TFS features is comparatively assessed using Receiver Operating Characteristic (ROC) analysis Area Under the Curve (AUC) measure. Experimental results confirm that the extended TFS features generally yield improved performance (up to 19%) when compared to the corresponding t-domain and f-domain features. The study also explores a second approach based on novel TF image (TFI) features that further improves TF-based classification of non-stationary signals. New TFI features are defined and extracted from the (t, f) domain; they include TF Hu invariant moments, TF Haralick features, and TF Local Binary Patterns (LBP). Using a state-of-the-art classifier, different metrics based on confusion matrix performance are compared for all extended TFS features and TFI features. Experimental results show the improved performance of TFI features over both TFS features and t-domain only or f-domain only features, for all TF representations and for all the considered performance metrics. The experiment is validated by comparing this new proposed methodology with a recent study, utilizing the same large and complex data set of EEG signals, and the same experimental setup. The resulting classification results confirm the superior performance of the proposed TFI features with accuracy improvement up to 5.52%.Scopu

Improving DOA Estimation Algorithms Using High-Resolution Quadratic Time-Frequency Distributions

This paper addresses the problem of direction of arrival (DOA) estimation and blind source separation (BSS) for nonstationary signals in the underdetermined case. These two problems are strongly related to the mixing matrix estimation problem. To deal with the nonstationary characteristics of signals, this study uses high-resolution quadratic time-frequency distributions (TFDs) to reduce cross-terms while keeping a good resolution for the construction of spatial TFDs. The main contributions of this paper are two-fold. First, the formulation of a statistical test for the noise thresholding step improves robustness and avoids the use of empirical parameters; this test performs multisource selection of the time-frequency points where the signal of interest is present. Second, an algorithm based on image processing methods performs an auto-source selection for mixing matrix estimation. The results on simulated signals demonstrate an improvement of 10 dB in terms of normalized mean square error for BSS and 7% in terms of relative error for DOA estimation over standard methods. 1 2017 IEEE.Scopu