Acoustic source localization (ASL) is pinpointing an acoustic source. ASL can reveal the point of impact of a foreign object or the point of crack initiation in a structure. ASL is necessary for continuous health monitoring of a structure. ASL in an anisotropic plate is a challenging task. This dissertation aims to investigate techniques that are currently being used to precisely determine an acoustic source location in an anisotropic plate without knowing its material properties. A new technique is developed and presented here to overcome the existing shortcomings of the acoustic source localization in anisotropic plates. It is done by changing the analysis perspective from the angular dependent group velocity of the wave and its straight line propagation to the wave front shapes and their geometric properties when a non-circular wave front is generated. Especially, 'rhombic wave front' and 'elliptical wave front' are dealt with because they are readily observed in highly anisotropic composite plates. Once each proposed technique meets the requirements of measurement, four sensor clusters in three different quadrants (recorded by 12 sensors) for the rhombus and at least three sensor clusters (recorded by 9 sensors) for the ellipse, accurate Acoustic Source Localization is obtained. It has been successfully demonstrated in the numerical simulations. In addition, a series of experimental tests demonstrate reliable and robust prediction performance of the developed new acoustic source localization technique.

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The angular location of an acoustic source can be estimated by measuring an acoustic direction of incidence based solely on the noise produced by the source. Methods for determining the direction of incidence based on sound intensity, the phase of cross-spectral functions, and cross-correlation functions are available. In this current work, we implement Dominant Frequency SElection (DFSE) algorithm. Direction of arrival (DOA) estimation usingmicrophone arrays is to use the phase information present in signals from microphones that are spatially separated. DFSE uses the phase difference between the Fourier transformedsignals to estimate the direction ofarrival (DOA)and is implemented using a three-element ’L’ shaped microphone array, linear microphone array, and planar 16-microphone array. This method is based on simply locating the maximum amplitude from each of the Fourier transformed signals and thereby deriving the source location by solving the set of non-linear least squares equations. For any pair of microphones, the surface on whichthe time difference ofarrival (TDOA) is constant is a hyperboloidoftwo sheets. Acoustic source localization algorithms typically exploit this fact by grouping all microphones into pairs, estimating the TDOA of each pair, then finding the point where all associated hyperboloids most nearly intersect. We make use of both closed-form solutions and iterative techniques to solve for the source location.Acoustic source positioned in 2-dimensional plane and 3-dimensional space have been successfully located.

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L’imagerie acoustique est une technique performante pour la localisation et la reconstruction de puissance des sources acoustiques en utilisant des mesures limitées au réseau des microphones. Elle est largement utilisée pour évaluer l’influence acoustique dans l’industrie automobile et aéronautique. Les méthodes d’imagerie acoustique impliquent souvent un modèle direct de propagation acoustique et l’inversion de ce modèle direct. Cependant, cette inversion provoque généralement un problème inverse mal-posé. Par conséquent, les méthodes classiques ne permettent d’obtenir de manière satisfaisante ni une haute résolution spatiale, ni une dynamique large de la puissance acoustique. Dans cette thèse, nous avons tout d’abord nous avons créé un modèle direct discret de la puissance acoustique qui devient alors à la fois linéaire et déterminé pour les puissances acoustiques. Et nous ajoutons les erreurs de mesures que nous décomposons en trois parties : le bruit de fond du réseau de capteurs, l’incertitude du modèle causée par les propagations à multi-trajets et les erreurs d’approximation de la modélisation. Pour la résolution du problème inverse, nous avons tout d’abord proposé une approche d’hyper-résolution en utilisant une contrainte de parcimonie, de sorte que nous pouvons obtenir une plus haute résolution spatiale robuste à aux erreurs de mesures à condition que le paramètre de parcimonie soit estimé attentivement. Ensuite, afin d’obtenir une dynamique large et une plus forte robustesse aux bruits, nous avons proposé une approche basée sur une inférence bayésienne avec un a priori parcimonieux. Toutes les variables et paramètres inconnus peuvent être estimées par l’estimation du maximum a posteriori conjoint (JMAP). Toutefois, le JMAP souffrant d’une optimisation non-quadratique d’importants coûts de calcul, nous avons cherché des solutions d’accélération algorithmique: une approximation du modèle direct en utilisant une convolution 2D avec un noyau invariant. Grâce à ce modèle, nos approches peuvent être parallélisées sur des Graphics Processing Unit (GPU) . Par ailleurs, nous avons affiné notre modèle statistique sur 2 aspects : prise en compte de la non stationarité spatiale des erreurs de mesures et la définition d’une loi a priori pour les puissances renforçant la parcimonie en loi de Students-t. Enfin, nous ont poussé à mettre en place une Approximation Variationnelle Bayésienne (VBA). Cette approche permet non seulement d’obtenir toutes les estimations des inconnues, mais aussi de fournir des intervalles de confiance grâce aux paramètres cachés utilisés par les lois de Students-t. Pour conclure, nos approches ont été comparées avec des méthodes de l’état-de-l’art sur des données simulées, réelles (provenant d’essais en soufflerie chez Renault S2A) et hybrides. / Acoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensor array. This technique can provide meaningful insights into performances, properties and mechanisms of acoustic sources. It has been widely used for evaluating the acoustic influence in automobile and aircraft industries. Acoustic imaging methods often involve in two aspects: a forward model of acoustic signal (power) propagation, and its inverse solution. However, the inversion usually causes a very ill-posed inverse problem, whose solution is not unique and is quite sensitive to measurement errors. Therefore, classical methods cannot easily obtain high spatial resolutions between two close sources, nor achieve wide dynamic range of acoustic source powers. In this thesis, we firstly build up a discrete forward model of acoustic signal propagation. This signal model is a linear but under-determined system of equations linking the measured data and unknown source signals. Based on this signal model, we set up a discrete forward model of acoustic power propagation. This power model is both linear and determined for source powers. In the forward models, we consider the measurement errors to be mainly composed of background noises at sensor array, model uncertainty caused by multi-path propagation, as well as model approximating errors. For the inverse problem of the acoustic power model, we firstly propose a robust super-resolution approach with the sparsity constraint, so that we can obtain very high spatial resolution in strong measurement errors. But the sparsity parameter should be carefully estimated for effective performance. Then for the acoustic imaging with large dynamic range and robustness, we propose a robust Bayesian inference approach with a sparsity enforcing prior: the double exponential law. This sparse prior can better embody the sparsity characteristic of source distribution than the sparsity constraint. All the unknown variables and parameters can be alternatively estimated by the Joint Maximum A Posterior (JMAP) estimation. However, this JMAP suffers a non-quadratic optimization and causes huge computational cost. So that we improve two following aspects: In order to accelerate the JMAP estimation, we investigate an invariant 2D convolution operator to approximate acoustic power propagation model. Owing to this invariant convolution model, our approaches can be parallelly implemented by the Graphics Processing Unit (GPU). Furthermore, we consider that measurement errors are spatially variant (non-stationary) at different sensors. In this more practical case, the distribution of measurement errors can be more accurately modeled by Students-t law which can express the variant variances by hidden parameters. Moreover, the sparsity enforcing distribution can be more conveniently described by the Student's-t law which can be decomposed into multivariate Gaussian and Gamma laws. However, the JMAP estimation risks to obtain so many unknown variables and hidden parameters. Therefore, we apply the Variational Bayesian Approximation (VBA) to overcome the JMAP drawbacks. One of the fabulous advantages of VBA is that it can not only achieve the parameter estimations, but also offer the confidential interval of interested parameters thanks to hidden parameters used in Students-t priors. To conclude, proposed approaches are validated by simulations, real data from wind tunnel experiments of Renault S2A, as well as the hybrid data. Compared with some typical state-of-the-art methods, the main advantages of proposed approaches are robust to measurement errors, super spatial resolutions, wide dynamic range and no need for source number nor Signal to Noise Ration (SNR) beforehand.

Speech is by far the most natural form of communication between human beings. It is intuitive, expressive and contains information at several cognitive levels. We as humans, are perceptive to several of these cognitive levels of information, as we can gather the information pertaining to the identity of the speaker, the speaker's gender, emotion, location, the language, and so on, in addition to the content of what is being spoken. This makes speech based human machine interaction (HMI), both desirable and challenging for the same set of reasons. For HMI to be natural for humans, it is imperative that a machine understands information present in speech, at least at the level of speaker identity, language, location in space, and the summary of what is being spoken. Although one can draw parallels between the human-human interaction and HMI, the two differ in their purpose. We, as humans, interact with a machine, mostly in the context of getting a task done more efficiently, than is possible without the machine. Thus, typically in HMI, controlling the machine in a specific manner is the primary goal. In this context, it can be argued that, HMI, with a limited vocabulary containing specific commands, would suffice for a more efficient use of the machine. In this thesis, we address the problem of ``Who spoke what and where", in the context of a machine understanding the information pertaining to identities of the speakers, their locations in space and the keywords they spoke, thus considering three levels of information - speaker identity (who), location (where) and keywords (what). This can be addressed with the help of multiple sensors like microphones, video camera, proximity sensors, motion detectors, etc., and combining all these modalities. However, we explore the use of only microphones to address this issue. In practical scenarios, often there are times, wherein, multiple people are talking at the same time. Thus, the goal of this thesis is to detect all the speakers, their keywords, and their locations in mixture signals containing speech from simultaneous speakers. Addressing this problem of ``Who spoke what and where" using only microphone signals, forms a part of acoustic scene analysis (ASA) of speech based acoustic events. We divide the problem of ``who spoke what and where" into two sub-problems: ``Who spoke what?" and ``Who spoke where". Each of these problems is cast in a generic latent variable (LV) framework to capture information in speech at different levels. We associate a LV to represent each of these levels and model the relationship between the levels using conditional dependency. The sub-problem of ``who spoke what" is addressed using single channel microphone signal, by modeling the mixture signal in terms of LV mass functions of speaker identity, the conditional mass function of the keyword spoken given the speaker identity, and a speaker-specific-keyword model. The LV mass functions are estimated in a Maximum likelihood (ML) framework using the Expectation Maximization (EM) algorithm using Student's-t Mixture Model (tMM) as speaker-specific-keyword models. Motivated by HMI in a home environment, we have created our own database. In mixture signals, containing two speakers uttering the keywords simultaneously, the proposed framework achieves an accuracy of 82 % for detecting both the speakers and their respective keywords. The other sub-problem of ``who spoke where?" is addressed in two stages. In the first stage, the enclosure is discretized into sectors. The speakers and the sectors in which they are located are detected in an approach similar to the one employed for ``who spoke what" using signals collected from a Uniform Circular Array (UCA). However, in place of speaker-specific-keyword models, we use tMM based speaker models trained on clean speech, along with a simple Delay and Sum Beamformer (DSB). In the second stage, the speakers are localized within the active sectors using a novel region constrained localization technique based on time difference of arrival (TDOA). Since the problem being addressed is a multi-label classification task, we use the average Hamming score (accuracy) as the performance metric. Although the proposed approach yields an accuracy of 100 % in an anechoic setting for detecting both the speakers and their corresponding sectors in two-speaker mixture signals, the performance degrades to an accuracy of 67 % in a reverberant setting, with a $60$ dB reverberation time (RT60) of 300 ms. To improve the performance under reverberation, prior knowledge of the location of multiple sources is derived using a novel technique derived from geometrical insights into TDOA estimation. With this prior knowledge, the accuracy of the proposed approach improves to 91 %. It is worthwhile to note that, the accuracies are computed for mixture signals containing more than 90 % overlap of competing speakers. The proposed LV framework offers a convenient methodology to represent information at broad levels. In this thesis, we have shown its use with three different levels. This can be extended to several such levels to be applicable for a generic analysis of the acoustic scene consisting of broad levels of events. It will turn out that not all levels are dependent on each other and hence the LV dependencies can be minimized by independence assumption, which will lead to solving several smaller sub-problems, as we have shown above. The LV framework is also attractive to incorporate prior knowledge about the acoustic setting, which is combined with the evidence from the data to derive the information about the presence of an acoustic event. The performance of the framework, is dependent on the choice of stochastic models, which model the likelihood function of the data given the presence of acoustic events. However, it provides an access to compare and contrast the use of different stochastic models for representing the likelihood function.