La végétation arborée sur les digues ou les barrages en remblai est un facteur de fragilité pouvant favoriser l'apparition de mécanismes de détérioration par érosion. Définir la structure géométrique des systèmes racinaires ainsi que la nature des sols dans lesquels ils croissent, à partir de méthodes géophysiques non destructives, est nécessaire afin d'appréhender les conséquences de leur développement sur la sécurité d'un ouvrage hydraulique. Des expériences en laboratoire ont d'abord permis de déterminer les propriétés acoustiques et électriques intrinsèques d'échantillons racinaires menant à l'identification de signatures indispensables à la discrimination de l'anomalie liée à la racine sur le terrain. Le montage de supports expérimentaux adaptés nous a conduits à progressivement étudier des paramètres d'influences en conditions contrôlées. Des essais en conditions semi-contrôlées sur un dispositif d'arbres plantés dans un sol homogène ont permis d'évaluer la pertinence des différentes méthodologies d'acquisition, tels que l'utilisation de la polarisation provoquée en tomographie de résistivité complexe ou encore la géométrie des capteurs pour la tomographie acoustique. Des traitements innovants tels que l'analyse en ondelettes sont associés afin d'exploiter la richesse des enregistrements. Les résultats obtenus ont été validés par le relevé des positions réelles des racines. Enfin, des campagnes d'auscultation sur un ouvrage réel ont été réalisées et ont permis de mettre en évidence une variabilité spatiale du corps de digue associée à la présence d'arbres. Une méthodologie adaptée au diagnostic géophysique de la végétation sur les ouvrages a été mise en place. / Woody vegetation from earth dikes or dams is a fragility factor which can promote mechanisms of degradation such as erosion. An accurate assessment of root system structure, from geophysical non-destructive methods, of root position into the embankment (depth, extension), and a good knowledge of soil conditions are critical in order to anticipate the consequences of vegetation development for the hydraulic structure’s safety. Laboratory experiments allowed determining intrinsic acoustical and electrical root properties leading to identify relevant signatures and discriminate anomalies related to roots in the field. The establishment of adapted experimental devices led us progressively to assess different parameters (roots mass, water content. . . ) under controlled conditions. Experiments in semi-controlled conditions with trees planted into a homogenous soil, were conducted to assess the relevance of different methodologies, such as the use of temporal induced polarization in complex resistivity tomography or the geometry of sensors for acoustical tomography. Innovative data processing such as wavelet analysis were used to valorize the rich database. The results were validated by the determination of actual root position.Finally, field investigations into an embankment have been performed to highlight a spatial variability of dike structures associated with trees presence. A methodology adapted to the geophysical diagnostic of vegetation roots in embankments was developed.

Ce travail de thèse porte sur l'étude de la faisabilité d'un capteur d'ondes acoustiques à base de laser à fibre. Cet hydrophone doit permettre la détection de faibles fluctuations de pression hydrostatique rencontrées à des grandes profondeurs marines. Sous l'influence d'une perturbation acoustique le laser est étiré; ce qui change le pas du réseau de Bragg et ainsi sa fréquence d'émission. L'information est donc codée dans la fréquence de la lumière émise. Le bruit de fréquence intrinsèque du laser doit alors être le plus bas possible d'où l'importance de pouvoir le mesurer dans la bande de fréquence [DC-20kHz]. Ainsi, nous effectuons une étude approfondie des propriétés physiques de l'émission laser en terme de cohérence temporelle. La sensibilité d'un laser à fibre à la pression acoustique est insuffisante. Il est primordial de concevoir un amplificateur mécanique qui permettra d'augmenter le rapport signal sur bruit du capteur. / This thesis focuses on the study of the feasibility of an acoustic wave sensor based on fiber laser. This hydrophone should allow the detection of low hydrostatic pressure fluctuations encountered in deep marine. Under the influence of an acoustic disturbance is stretched the laser; thereby changing the pitch of the Bragg grating and thus its transmission frequency. The information is coded in the frequency of the emitted light. The intrinsic frequency noise of the laser must be as low as possible so it is important to be able to measure in the frequency band of $[DC-20KHz]$. Thus we conduct a thorough study of the physical properties of the laser emission in terms of temporal coherence.The sensitivity of a fiber laser acoustic pressure is insufficient. It is essential to design a mechanical amplifier for to increase the signal to noise ratio of the sensor.

El objeto de esta tesis se centra el estudio del comportamiento acústico de estructuras multicapa, utilizando como herramienta, entre otras, la denominada "Holografía acústica de campo cercano" (NAH, Nearfield Acoustical Holography). Las conclusiones obtenidas y las herramientas desarrolladas en este estudio tienen implicaciones en dos ámbitos, el aislamiento acústico con particiones ligeras multicapa, y la optimización de la radiación de sistemas electroacústicos basados en la vibración de un panel provocada por un elemento excitador de tipo dinámico (caso de los altavoces planos). El proceso para conseguir predicciones acertadas del comportamiento de estructuras multicapa, tanto desde el punto de vista de la aplicación acústica de la edificación, como el electroacústico (mejora de prestaciones de altavoces), debe partir, necesariamente de un conocimiento preciso de las características acústicas de los componentes, es decir, cada una de las capas. Este estudio constituye la primera fase de la investigación y del trabajo. La validación de las teorías sobre el aislamiento acústico lleva contigo el ensayo de diferentes materiales y configuraciones. El objeto es aumentar al máximo el índice de aislamiento de una cierta configuración, intentado disminuir en lo posible los costes. El estudio del comportamiento acústico de distintas configuraciones suele realizarse en cámara de transmisión. Los resultados de las medidas en estas condiciones no tienen por qué coincidir con los obtenidos con medidas "in situ", ya que las condiciones de montaje son hasta cierto punto incontrolables. Existen diversos trabajos donde se desarrollan los diferentes modelos, teorías y técnicas de medición con este fin, desde modelos y teorías para caracterizar materiales absorbentes hasta métodos de caracterización de materiales absorbentes medidos en el tubo de Kundt. También existen medidas de una determinado variable acústica, permiten obtener los parámetros de entrada necesarios para los progr / Escuder Silla, EM. (2005). Estudio del comportamiento acústico de estructuras multicapa mediante nah [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1860 / Palancia

In search for quieter engines there is a need for a better understanding of the acoustic properties of engine intake and exhaust system components. Besides mufflers which have the purpose of reducing pressure pulses originating from the internal combustion (IC) engine, there are many components in a modern car exhaust and intake system, e.g., air-filters, coolers, catalytic converters, particulate filters - all having an effect on the pressure pulses or sound field in the system. In this work the focus is on the turbocharged IC-engine where both, sound scattering (reflection and transmission) and sound generation from automotive turbochargers are studied. In addition, sound reflection from an open ended pipe, such as the tailpipe of an IC-engine exhaust is investigated.             Accurate and efficient methods to fully characterize turbochargers by measuring the acoustic two-port have been developed.  Compared to earlier work, a number of modifications are suggested for improving the quality of the results. A study on three different automotive turbochargers is also presented, including data for sound scattering for both the compressor and turbine. The results for the transmission of sound, which is of interest for the ability of a turbocharger to reduce noise coming from the engine, is plotted for all tested cases against a dimensionless frequency scale (Helmholtz-number). This makes it possible to generalize the result in order to draw conclusions about the behavior for any turbocharger.              The sound generation was also studied and three different methods to estimate the sound power are suggested. The methods were used to investigate sound generation at different operating points and identify source mechanisms for a turbocharger compressor.             An accurate method for measuring the reflection of plane acoustic waves from a pipe termination in a duct with hot gas flow has been developed and tested. Representing the acoustical conditions at an exhaust tail-pipe, the data obtained is important for effective modeling of exhaust systems. The experimental results of the reflection coefficient were compared with Munt`s theory on flow duct openings. The measurements were carried out for air jet velocities up to Mach 0.4 and for flow temperatures up to 100°C in order to study temperature effects on the reflection properties. It was concluded, that the experimental results agree well with the Munt theory.

We are seeing a revolution in computing and artificial intelligence; intelligent machines have become ingrained in and improved every aspect of our lives. Despite the increasing number of intelligent devices and breakthroughs in artificial intelligence, we have yet to achieve truly intelligent environments. Audio is one of the most common sensing and actuation modalities used in intelligent devices. In this thesis, we focus on how we can more robustly integrate audio intelligence into a wide array of resource-constrained platforms that enable more intelligent environments. We present systems and methods for adaptive audio filtering that enables us to more robustly embed acoustic intelligence into a wide range of real time and resource-constrained mobile, embedded, and cyber-physical systems that are adaptable to a wide range of different applications, environments, and scenarios. First, we introduce methods for embedding audio intelligence into wearables, like headsets and helmets, to improve pedestrian safety in urban environments by using sound to detect vehicles, localize vehicles, and alert pedestrians well in advance to give them enough time to avoid a collision. We create a segmented architecture and data processing pipeline that partitions computation between embedded front-end platform and the smartphone platform. The embedded front-end hardware platform consists of a microcontroller and commercial-off-the shelf (COTS) components embedded into a headset and samples audio from an array of four MEMS microphones. Our embedded front-end platform computes a series of spatiotemporal features used to localize vehicles: relative delay, relative power, and zero crossing rate. These features are computed in the embedded front-end headset platform and transmitted wirelessly to the smartphone platform because there is not enough bandwidth to transmit more than two channels of raw audio with low latency using standard wireless communication protocols, like Bluetooth Low-Energy. The smartphone platform runs machine learning algorithms to detect vehicles, localize vehicles, and alert pedestrians. To help reduce power consumption, we integrate an application specific integrated circuit into our embedded front-end platform and create a new localization algorithm called angle via polygonal regression (AvPR) that combines the physics of audio waves, the geometry of a microphone array, and a data driven training and calibration process that enables us to estimate the high resolution direction of the vehicle while being robust to noise resulting from movements in the microphone array as we walk the streets. Second, we explore the challenges in adapting our platforms for pedestrian safety to more general and noisier scenarios, namely construction worker safety sounds of nearby power tools and machinery that are orders of magnitude greater than that of a distant vehicle. We introduce an adaptive noise filtering architecture that allows workers to filter out construction tool sounds and reveal low-energy vehicle sounds to better detect them. Our architecture combines the strengths of both the physics of audio waves and data-driven methods to more robustly filter out construction sounds while being able to run on a resource-limited mobile and embedded platform. In our adaptive filtering architecture, we introduce and incorporate a data-driven filtering algorithm, called probabilistic template matching (PTM), that leverages pre-trained statistical models of construction tools to perform content-based filtering. We demonstrate improvements that our adaptive filtering architecture brings to our audio-based urban safety wearable in real construction site scenarios and against state-of-art audio filtering algorithms, while having a minimal impact on the power consumption and latency of the overall system. We also explore how these methods can be used to improve audio privacy and remove privacy-sensitive speech from applications that have no need to detect and analyze speech. Finally, we introduce a common selective audio filtering platform that builds upon our adaptive filtering architecture for a wide range of real-time mobile, embedded, and cyber-physical applications. Our architecture can account for a wide range of different sounds, model types, and signal representations by integrating an algorithm we present called content-informed beamforming (CIBF). CIBF combines traditional beamforming (spatial filtering using the physics of audio waves) with data driven machine learning sound detectors and models that developers may already create for their own applications to enhance and filter out specified sounds and noises. Alternatively, developers can also select sounds and models from a library we provide. We demonstrate how our selective filtering architecture can improve the detection of specific target sounds and filter out noises in a wide range of application scenarios. Additionally, through two case studies, we demonstrate how our selective filtering architecture can easily integrate into and improve the performance of real mobile and embedded applications over existing state-of-art solutions, while having minimal impact on latency and power consumption. Ultimately, this selective filtering architecture enables developers and engineers to more easily embed robust audio intelligence into common objects found around us and resource-constrained systems to create more intelligent environments.

SAAB has developed an autonomous underwater vehicle that can mimic a conventional submarine for military fleets to exercise anti-submarine warfare. The AUV actively emits amplified versions of received sonar pulses to create the illusion of being a larger object. To prevent acoustic feedback, the AUV must distinguish between the sound to be actively responded to and its emitted signal. This master thesis has examined techniques aimed at preventing the AUV from responding to previously emitted signals to avoid acoustical feedback, without relying on prior knowledge of either the received signal or the signal emitted by the AUV. The two primary types of algorithms explored for this problem include blind source separation and adaptive filtering. The adaptive filters based on Leaky Least Mean Square and Kalman have shown promising results in attenuating the active response from the received signal. The adaptive filters utilize the fact that a certain hydrophone primarily receives the active response. This hydrophone serves as an estimate of the active response since the signal it captures is considered unknown and is to be removed. The techniques based on blind source separation have utilized the recordings of three hydrophones placed at various locations of the AUV to separate and estimate the received signal from the one emitted by the AUV. The results have demonstrated that neither of the reviewed methods is suitable for implementation on the AUV. The hydrophones are situated at a considerable distance from each other, resulting in distinct time delays between the reception of the two signals. This is usually referred to as a convolutive mixture. This is commonly solved using the frequency domain to transform the convolutive mixture to an instantaneous mixture. However, the fact that the signals share the same frequency spectrum and are adjacent in time has proven highly challenging.

Indiana University-Purdue University Indianapolis (IUPUI) / In order to conform to pollutant-control regulations and minimize NOx emissions, modern household boilers and central heating systems are moving toward premixed combustors. These combustors have been successful with regards to emissions along with efficiency. However, their implementation has been associated with acoustical instability problems that could be solved through precise optimization in design rather than trial and error experimentation. This thesis introduces an experimental apparatus, which is designed to investigate the acoustic instability problem at the flame level. The goal is an experimental determination of the flame transfer function and comparison of the experimental data with a theoretical model of the flame. An experimental procedure is designed to diagnose the origins of the combustion instabilities by measurement of the flame transfer function. This research is carried out in three steps. The first step is to understand the acoustic instability problem through study of the theoretical models of the flame transfer function and selection of a model, which is most functional in industrial applications. A xiii measurement technique for the flame transfer function is developed according to the required accuracy in measurements, repeatability, and configurability for a wide range of operating conditions. Subsequently, an experimental apparatus is designed to accommodate the flame transfer function measurement technique. The components of the acoustic system are carefully sized to achieve precise measurement of the system parameters such as flows, pressures, and acoustic responses, and the apparatus is built. The apparatus is operated to measure the flame transfer function at several operating conditions. The experimentally measured flame transfer function is compared with a theoretical model for further verification. The experimental apparatus provides an improved assessment of the acoustic instability problem for industrial applications.

Scan-based near-field acoustical holography (NAH) is applied to partially correlated sources. Partial field decomposition via the virtual coherence method is used to implement the scan-based NAH. The background and theory of these methods are developed. Multiple stationary reference microphones are required for the partial field decomposition. Guidelines for reference microphone placement in the literature thus far have been limited. Improved guidelines for reference microphones are given after the results of two sets of experiments. The first set involves discrete, partially correlated sources, both physical and numerical. The second set of experiments is strictly numerical and involves continuous sources. Fewer microphones are required for partially correlated sources as compared to completely uncorrelated sources. Reference microphone number is found to be more critical to reducing holography reconstruction errors than is placement or location. For the continuous results, an appropriate figure of merit is created: reference microphones per coherence length. Based upon the definition of coherence length, two reference microphones per coherence length are required to minimize reconstruction error. Further practical reference microphone guidelines are given. These guidelines are to assist in preparing for a full-scale application of scan-based near-field acoustical holography to a military aircraft jet.

Recent advances in near-field acoustical holography (NAH) have expanded the theory to interior spaces where multiple sources and/or reflections are present. In 1990, Tamura presented the spatial Fourier transform separation method to measure the reflection coefficient at oblique angles using two measurement planes in the wave number domain. This paper adapts the spatial Fourier transform separation method for application in interior NAH. A practical exploration of important experimental parameters is performed, which include the relative amplitudes of primary and disturbing sources, the measurement plane separation distance, and an acceptable noise floor. This technique is successfully applied in a reverberant environment to reconstruct the velocity of a clamped vibrating plate. NAH methods based on the measurement of pressure and particle velocity have led to the ability to reduce the required measurement locations. Other recent advances in NAH have expanded the theory to interior spaces where multiple sources and/or reflections are present. This paper investigates the use of interpolation techniques to reduce the required measurement locations for interior NAH. Specifically, the benefits of a bi-cubic Hermite surface patch interpolation are discussed and compared to other interpolation routines. Although the required inputs for the Hermite interpolation can be measured using a variety of devices, a scanning six-microphone probe in a tetrahedral configuration is suggested. The six microphones are utilized to simultaneously sample pressure on two parallel planes and estimate the pressure gradients on both of these planes. The two interpolated measurement holograms are used to separate the incoming and outgoing waves using the spatial Fourier-transform method. Analytical simulations of simply supported plates are shown as well as experimental results in a reverberation room to characterize the reduction in measurement locations. Depending on the spatial frequency of the hologram, a measurement location reduction of 20–80% was observed.

<p dir="ltr">The onset of smart manufacturing signifies a crucial shift in the industrial landscape, underscoring the pressing need for systems capable of adapting to and managing the complex dynamics of modern production environments. In this context, the importance of smart monitoring becomes increasingly apparent, serving as a vital tool for ensuring operational efficiency and reliability. Inspired by the critical role of auditory perception in human decision-making, this study investigated the application of machine sound recognition for practical use in manufacturing environments. Addressing the challenge of utilizing machine sounds in the loud noises of factories, the study employed an Internal Sound Sensor (ISS).</p><p dir="ltr">The study examined how sound propagates through structures and further explored acoustic characteristics of the ISS, aiming to apply these findings in machine monitoring. To leverage the ISS effectively and achieve a higher level of monitoring, a smart sound monitoring framework was proposed to integrate sound monitoring with machine data and human-machine interface. Designed for applicability and cost effectiveness, this system employs real-time edge computing, making it adaptable for use in various industrial settings.</p><p dir="ltr">The proposed framework and ISS deployed across a diverse range of production environments, showcasing a leap forward in the integration of smart technologies in manufacturing. Their application extends beyond continuous manufacturing to include discrete manufacturing systems, demonstrating adaptability. By analyzing sound signals from various production equipment, this study delves into developing machine sound recognition models that predict operational states and productivity, aiming to enhance manufacturing efficiency and oversight on real factory floors. This comprehensive and practical approach underlines the framework's potential to revolutionize operational management and manufacturing productivity. The study progressed to integrating manufacturing context with sound data, advancing towards high-level monitoring for diagnostic predictions and digital twin. This approach confirmed sound recognition's role in manufacturing diagnostics, laying a foundation for future smart monitoring improvements.</p>