Spectral Analysis for Signal Detection and Classification : Reducing Variance and Extracting Features

Spectral analysis encompasses several powerful signal processing methods. The papers in this thesis present methods for finding good spectral representations, and methods both for stationary and non-stationary signals are considered. Stationary methods can be used for real-time evaluation, analysing shorter segments of an incoming signal, while non-stationary methods can be used to analyse the instantaneous frequencies of fully recorded signals. All the presented methods aim to produce spectral representations that have high resolution and are easy to interpret. Such representations allow for detection of individual signal components in multi-component signals, as well as separation of close signal components. This makes feature extraction in the spectral representation possible, relevant features include the frequency or instantaneous frequency of components, the number of components in the signal, and the time duration of the components. Two methods that extract some of these features automatically for two types of signals are presented in this thesis. One adapted to signals with two longer duration frequency modulated components that detects the instantaneous frequencies and cross-terms in the Wigner-Ville distribution, the other for signals with an unknown number of short duration oscillations that detects the instantaneous frequencies in a reassigned spectrogram. This thesis also presents two multitaper methods that reduce the influence of noise on the spectral representations. One is designed for stationary signals and the other for non-stationary signals with multiple short duration oscillations. Applications for the methods presented in this thesis include several within medicine, e.g. diagnosis from analysis of heart rate variability, improved ultrasound resolution, and interpretation of brain activity from the electroencephalogram

Object characterisation using wideband sonar pulses

Characterisation of objects in an underwater environment is challenging. Success in the task can be beneficial in a variety of scenarios, which include oil and gas pipe maintenance, archaeology, and assistance to general underwater object identification. This work focuses on object characterisation, providing a solution for material identification. To do this, one must sense the underwater environment for which there are several different ways. Some of the most popular rely on sonar images. These provide limited information about the objects,mostly the shape, size and distance to the object. The study of acoustic wave scattering over a wide frequency range provides more information about the targets characteristics. This work builds on the principles of sound scattering. An acoustic echo reflected from an object has a different pulse shape and frequency composition than its initial pulse. These changes in the pulse are due to the interaction of the sound wave with an object during the reflection process and the pulses interaction with the transmission medium. Study of the reflected pulse can provide information about physical properties such as size, material and shell thickness. The objects used in this work are limited to spherical shells made of a variety of materials, and filled with different liquids or air. The task of material identification is approached in two different ways. The first one is a machine learning based approach. The classification is not based on the object’s shape, but on its physical properties including the composition material. Two approaches will be presented: one, where the spherical shell is described by the echo’s representation in time frequency domain and one, where it is described by the form function. The objects are classified using a number of machine learning techniques including support vector machine, gradient boosting and neural networks. The machine learning approaches give good results for a number of tasks, but are not sufficient to distinguish between materials with similar properties, like water and salt water. An alternative solution is presented in this thesis, which identifies the filler and the shell materials separately. This material identification approach is based on the timing of the sound scattering components. The echo reflected from an object is formed by a number of processes. The information about these processes can be extracted from the echoes and used to identify the material. This approach does not require any training data and shows good results, which are demonstrated on both the simulated and experimental data. This work focuses on object characterisation, providing a solution for material identification using underwater acoustics and signal processing techniques

Novel solutions to classical signal processing problems in optimization framework

Cataloged from PDF version of article.Novel approaches for three classical signal processing problems in optimization framework are proposed to provide further flexibility and performance improvement. In the first part, a new technique, which uses Hermite-Gaussian (HG) functions, is developed for analysis of signals, whose components have non-overlapping compact time-frequency supports. Once the support of each signal component is properly transformed, HG functions provide optimal representations. Conducted experiments show that proposed method provides reliable identification and extraction of signal components even under severe noise cases. In the second part, three different approaches are proposed for designing a set of orthogonal pulse shapes for ultra-wideband communication systems with wideband antennas. Each pulse shape is modelled as a linear combination of time shifted and scaled HG functions. By solving the constructed optimization problems, high energy pulse shapes, which maintain orthogonality at the receiver with desired timefrequency characteristics are obtained. Moreover, by showing that, derivatives of HG functions can be represented as a linear combination of HGs, a simple optimal correlating receiver structure is proposed. In the third part, two different methods for phase-only control of array antennas based on semidefinite modelling are proposed. First, antenna pattern design problem is formulated as a non-convex quadratically constraint quadratic problem (QCQP). Then, by relaxing the QCQP formulation, a convex semidefinite problem (SDP) is obtained. For moderate size arrays, a novel iterative rank refinement algorithm is proposed to achieve a rank-1 solution for the obtained SDP, which is the solution to the original QCQP formulation. For large arrays an alternating direction method of multipliers (ADMM) based solution is developed. Conducted experiments show that both methods provide effective phase settings, which generate beam patterns under highly flexible constraints.Alp, Yaşar KemalPh.D

Discrimination à longue distance des signatures vocales individuelles chez un oiseau chanteur : des contraintes de propagation au substrat neuronal

In communication systems, one of the biggest challenges is that the information encoded by the emitter is always modified before reaching the receiver, who has to process this altered information in order to recover the intended message. In acoustic communication particularly, the transmission of sound through the environment is a major source of signal degradation, caused by attenuation, absorption and reflections, all of which lead to decreases in the signal relative to the background noise. How animals deal with the need for exchanging information in spite of constraining conditions has been the subject of many studies either at the emitter or at the receiver's levels. However, a more integrated research about auditory scene analysis has seldom been used, and is needed to address the complexity of this process. The goal of my research was to use a transversal approach to study how birds adapt to the constraints of long distance communication by investigating the information coding at the emitter's level, the propagation-induced degradation of the acoustic signal, and the discrimination of this degraded information by the receiver at both the behavioral and neural levels. Taking into account the everyday issues faced by animals in their natural environment, and using stimuli and paradigms that reflected the behavioral relevance of these challenges, has been the cornerstone of my approach. Focusing on the information about individual identity in the distance calls of zebra finches Taeniopygia guttata, I investigated how the individual vocal signature is encoded, degraded, and finally discriminated, from the emitter to the receiver. This study shows that the individual signature of zebra finches is very resistant to propagation-induced degradation, and that the most individualized acoustic parameters vary depending on distance. Testing female birds in operant conditioning experiments, I showed that they are experts at discriminating between the degraded vocal signatures of two males, and that they can improve their ability substantially when they can train over increasing distances. Finally, I showed that this impressive discrimination ability also occurs at the neural level: we found a population of neurons in the avian auditory forebrain that discriminate individual voices with various degrees of propagation-induced degradation without prior familiarization or training. The finding of such a high-level auditory processing, in the primary auditory cortex, opens a new range of investigations, at the interface of neural processing and behaviorL'un des plus grands défis posés par la communication est que l'information codée par l'émetteur est toujours modifiée avant d'atteindre le récepteur, et que celui-ci doit traiter cette information altérée afin de recouvrer le message. Ceci est particulièrement vrai pour la communication acoustique, où la transmission du son dans l'environnement est une source majeure de dégradation du signal, ce qui diminue l'intensité du signal relatif au bruit. La question de savoir comment les animaux transmettent l'information malgré ces conditions contraignantes a été l'objet de nombreuses études, portant soit sur l'émetteur soit sur le récepteur. Cependant, une recherche plus intégrée sur l'analyse de scènes auditives est nécessaire pour aborder cette tâche dans toute sa complexité. Le but de ma recherche était d'utiliser une approche transversale afin d'étudier comment les oiseaux s'adaptent aux contraintes de la communication à longue distance, en examinant le codage de l'information au niveau de l'émetteur, les dégradations du signal acoustiques dues à la propagation, et la discrimination de cette information dégradée par le récepteur, au niveau comportemental comme au niveau neuronal. J'ai basé mon travail sur l'idée de prendre en compte les problèmes réellement rencontrés par les animaux dans leur environnement naturel, et d'utiliser des stimuli reflétant la pertinence biologique des problèmes posés à ces animaux. J'ai choisi de me focaliser sur l'information d'identité individuelle contenue dans le cri de distance des diamants mandarins (Taeniopygia guttata) et d'examiner comment la signature vocale individuelle est codée, dégradée, puis discriminée et décodée, depuis l'émetteur jusqu'au récepteur. Cette étude montre que la signature individuelle des diamants mandarins est très résistante à la propagation, et que les paramètres acoustiques les plus individualisés varient selon la distance considérée. En testant des femelles dans les expériences de conditionnement opérant, j'ai pu montrer que celles-ci sont expertes pour discriminer entre les signature vocales dégradées de deux mâles, et qu'elles peuvent s'améliorer en s'entraînant. Enfin, j'ai montré que cette capacité de discrimination impressionnante existe aussi au niveau neuronal : nous avons montré l'existence d'une population de neurones pouvant discriminer des voix individuelles à différent degrés de dégradation, sans entrainement préalable. Ce niveau de traitement évolué, dans le cortex auditif primaire, ouvre la voie à de nouvelles recherches, à l'interface entre le traitement neuronal de l'information et le comportemen

Automatic time-frequency analysis of echolocation signals using the matched Gaussian multitaper spectrogram

High-resolution time-frequency (TF) images of multi-component signals are of great interest for visualization, feature extraction and estimation. The matched Gaussian multitaper spectrogram has been proposed to optimally resolve multi-component transient functions of Gaussian shape. Hermite functions are used as multitapers and the weights of the different spectrogram functions are optimized. For a fixed number of multitapers, the optimization gives the approximate Wigner distribution of the Gaussian shaped function. Increasing the number of multitapers gives a better approximation, i.e. a better resolution, but the cross-terms also become more prominent for close TF components. In this submission, we evaluate a number of different concentration measures to automatically estimate the number of multitapers resulting in the optimal spectrogram for TF images of dolphin echolocation signals. The measures are evaluated for different multi-component signals and noise levels and a suggestion of an automatic procedure for optimal TF analysis is given. The results are compared to other well known TF estimation algorithms and examples of real data measurements of echolocation signals from a beluga whale (Delphinapterus leucas) are presented