A Bayesian sparse inference approach in near-field wideband aeroacoustic imaging

International audienceRecently improved deconvolution methods using sparse regularization achieve high spatial resolution in aeroacoustic imaging in the low Signal-to-Noise Ratio (SNR), but sparse prior and model parameters should be optimized to obtain super resolution and be robust to sparsity constraint. In this paper, we propose a Bayesian Sparse Inference Approach in Aeroacoustic Imaging (BSIAAI) to reconstruct both source powers and positions in poor SNR cases, and simultaneously estimate background noise and model parameters. Double Exponential prior model is selected for source spatial distribution and hyper-parameters are estimated by Joint Maximized A Posterior criterion and Bayesian Expectation and Minimization algorithm. On simulated and wind tunnel data, proposed approach is well applied for near-field wideband monopole and extended source imaging. Comparing to several classical methods, proposed approach is robust to noise, super resolution, wide dynamic range, and source number and SNR are not needed

Bayesian sparse regularization in near-field wideband aeroacoustic imaging for wind tunnel test

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An efficient variational Bayesian inference approach via Studient's-t priors for acoustic imaging in colored noises

International audienceAcoustic imaging is a powerful tool to localize and reconstruct source powers using microphone array. However, it often involves the ill-posed inversions and becomes too time-consuming to obtain high spatial resolutions. In this paper, we firstly propose a shift-invariant convolution model to approximate the forward model of acoustic power propagation. The convolution kernel is derived from the Symmetric Toepliz Block Toepliz (STBT) structure of propagation matrix. Then we propose a hierarchical Bayesian inference approach via Variational Bayesian Approximation (VBA) criterion in order to achieve robust acoustic imaging in colored background noises. For super spatial resolution and wide dynamic power range, we explore the Student's-t prior on the acoustic power distribution thanks to the sparsity and heavy tail of prior model. Colored noise distributions are also modeled by the Student's-t prior, and this does not excessively penalize large model errors as the Gaussian white prior does. Finally proposed 2D convolution model and VBA approach are validated through simulations and real data from wind tunnel compared to classical methods

Bayesian approach in acoustic source localization and imaging

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.PARIS11-SCD-Bib. électronique (914719901) / SudocSudocFranceF

2D convolution model using (in)variant kernels for fast acoustic imaging

International audienceAcoustic imaging is an advanced technique for acoustic source localization and power reconstruction using limited measurements at microphone sensors. The acoustic imaging methods often involve in two aspects: one is to build up a forward model of acoustic power propagation which requires tremendous matrix multiplications due to large dimension of the power propagation matrix; the other is to solve an inverse problem which is usually ill-posed and time consuming. In this paper, our main contribution is to propose to use 2D convolution model for fast acoustic imaging. We find out that power propagation ma-trix seems to be a quasi-Symmetric Toeplitz Block Toeplitz (STBT) matrix in the far-field condition, so that the (in)variant convolution kernels (sizes and values) can be well derived from this STBT matrix. For method validation, we use simulated and real data from the wind tunnel S2A (France) experiment for acoustic imaging on vehicle surface

Clustering Inverse Beamforming and multi-domain acoustic imaging approaches for vehicles NVH

Il rumore percepito all’interno della cabina di un veicolo è un aspetto molto rilevante nella valutazione della sua qualità complessiva. Metodi sperimentali di acoustic imaging, quali beamforming e olografia acustica, sono usati per identificare le principali sorgenti che contribuiscono alla rumorosità percepita all’interno del veicolo. L’obiettivo della tesi proposta è di fornire strumenti per effettuare dettagliate analisi quantitative tramite tali tecniche, ad oggi relegate alle fasi di studio preliminare, proponendo un approccio modulare che si avvale di analisi dei fenomeni vibro-acustici nel dominio della frequenza, del tempo e dell’angolo di rotazione degli elementi rotanti tipicamente presenti in un veicolo. Ciò permette di ridurre tempi e costi della progettazione, garantendo, al contempo, una maggiore qualità del pacchetto vibro-acustico. L’innovativo paradigma proposto prevede l’uso combinato di algoritmi di pre- e post- processing con tecniche inverse di acoustic imaging per lo studio di rilevanti problematiche quali l’identificazione di sorgenti sonore esterne o interne all’abitacolo e del rumore prodotto da dispositivi rotanti. Principale elemento innovativo della tesi è la tecnica denominata Clustering Inverse Beamforming. Essa si basa su un approccio statistico che permette di incrementare l’accuratezza (range dinamico, localizzazione e quantificazione) di una immagine acustica tramite la combinazione di soluzioni, del medesimo problema inverso, ottenute considerando diversi sotto-campioni dell’informazione sperimentale disponibile, variando, in questo modo, in maniera casuale la sua formulazione matematica. Tale procedimento garantisce la ricostruzione nel dominio della frequenza e del tempo delle sorgenti sonore identificate. Un metodo innovativo è stato inoltre proposto per la ricostruzione, ove necessario, di sorgenti sonore nel dominio dell’angolo. I metodi proposti sono stati supportati da argomentazioni teoriche e validazioni sperimentali su scala accademica e industriale.The interior sound perceived in vehicle cabins is a very important attribute for the user. Experimental acoustic imaging methods such as beamforming and Near-field Acoustic Holography are used in vehicles noise and vibration studies because they are capable of identifying the noise sources contributing to the overall noise perceived inside the cabin. However these techniques are often relegated to the troubleshooting phase, thus requiring additional experiments for more detailed NVH analyses. It is therefore desirable that such methods evolve towards more refined solutions capable of providing a larger and more detailed information. This thesis proposes a modular and multi-domain approach involving direct and inverse acoustic imaging techniques for providing quantitative and accurate results in frequency, time and angle domain, thus targeting three relevant types of problems in vehicles NVH: identification of exterior sources affecting interior noise, interior noise source identification, analysis of noise sources produced by rotating machines. The core finding of this thesis is represented by a novel inverse acoustic imaging method named Clustering Inverse Beamforming (CIB). The method grounds on a statistical processing based on an Equivalent Source Method formulation. In this way, an accurate localization, a reliable ranking of the identified sources in frequency domain and their separation into uncorrelated phenomena is obtained. CIB is also exploited in this work for allowing the reconstruction of the time evolution of the sources sought. Finally a methodology for decomposing the acoustic image of the sound field generated by a rotating machine as a function of the angular evolution of the machine shaft is proposed. This set of findings aims at contributing to the advent of a new paradigm of acoustic imaging applications in vehicles NVH, supporting all the stages of the vehicle design with time-saving and cost-efficient experimental techniques. The proposed innovative approaches are validated on several simulated and real experiments

Estimating Sparse Representations from Dictionaries With Uncertainty

In the last two decades, sparse representations have gained increasing attention in a variety of engineering applications. A sparse representation of a signal requires a dictionary of basic elements that describe salient and discriminant features of that signal. When the dictionary is created from a mathematical model, its expressiveness depends on the quality of this model. In this dissertation, the problem of estimating sparse representations in the presence of errors and uncertainty in the dictionary is addressed. In the first part, a statistical framework for sparse regularization is introduced. The second part is concerned with the development of methodologies for estimating sparse representations from highly redundant dictionaries along with unknown dictionary parameters. The presented methods are illustrated using applications in direction finding and fiber-optic sensing. They serve as illustrative examples for investigating the abstract problems in the theory of sparse representations. Estimating a sparse representation often involves the solution of a regularized optimization problem. The presented regularization framework offers a systematic procedure for the determination of a regularization parameter that accounts for the joint effects of model errors and measurement noise. It is determined as an upper bound of the mean-squared error between the corrupted data and the ideal model. Despite proper regularization, the quality and accuracy of the obtained sparse representation remains affected by model errors and is indeed sensitive to changes in the regularization parameter. To alleviate this problem, dictionary calibration is performed. The framework is applied to the problem of direction finding. Redundancy enables the dictionary to describe a broader class of observations but also increases the similarity between different entries, which leads to ambiguous representations. To address the problem of redundancy and additional uncertainty in the dictionary parameters, two strategies are pursued. Firstly, an alternating estimation method for iteratively determining the underlying sparse representation and the dictionary parameters is presented. Also, theoretical bounds for the estimation errors are derived. Secondly, a Bayesian framework for estimating sparse representations and dictionary learning is developed. A hierarchical structure is considered to account for uncertainty in prior assumptions. The considered model for the coefficients of the sparse representation is particularly designed to handle high redundancy in the dictionary. Approximate inference is accomplished using a hybrid Markov Chain Monte Carlo algorithm. The performance and practical applicability of both methodologies is evaluated for a problem in fiber-optic sensing, where a mathematical model for the sensor signal is compiled. This model is used to generate a suitable parametric dictionary

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio