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In this thesis, localization of the underwater sound sources using matched field processing technique is considered. Localization of the underwater sound sources is one of the most important problems encountered in underwater acoustics and signal processing. Many techniques were developed to localize sources in range, depth and bearing angle. However, most of these techniques do not consider or only slightly takes into account the environmental factors that dramatically effect the propagation of underwater sound. Matched field processing has been developed as a technique that fully considers the environmental factors. Matched field processing has proven to be successful in many applications such as localization of sources in range and depth, the determination of environmental parameters, and the evaluation of model accuracies. In this study, first a comparative analysis of narrowband matched field processors is given. Namely four main processors: Bartlett processor, Minimum Variance Distortionless Response (MVDR) processor, MVDR with neighboring location constraints and MVDR with environmental perturbation constraints are compared in terms of their probability of correct localization under certain environmental conditions. Secondly, a performance assesment for the most common broadband matched field processors is made. The correct localization performances for incoherent broadband matched field processor, Tolstoy/Michalopoulo&#039 / s coherent matched field processor and broadband matched field processor with environmental perturbation constraints is given for certain environmental conditions. Finally, a new weighting approach to combine data for broadband matched field processing is introduced. The fact that information from different frequencies may have different reliability depending on the environmental conditions is considered to develop a weighting scheme. It is shown that a performance gain compared to existing processors can be achieved by using the weighting scheme introduced in this study.

Thesis (M.S.) -- University of Maryland, College Park, 1999. / Thesis research directed by Institute for Systems Research. "M.S. 99-6." Includes bibliographical references (leaves 75-78). Available also online as a PDF file via the World Wide Web.

Time reversal is a method often used to focus sound to a desired location, and works best in a reverberant environment. The effect of focus location within a reverberant environment is presented first, revealing that proximity to reflecting surfaces has a significant effect on the amplitude of the focus both experimentally and when using a modal summation model. These effects are a primary component to creating focus signals at high amplitudes. High-amplitude focusing experiments show that when multiple sources are used simultaneously to generate a focus, a peak amplitude pressure spike of 200 dB can be achieved in air. A pressure spike of this amplitude has multiple nonlinear characteristics, and an investigation into the spatiotemporal features and harmonic content of these signals was conducted. The peak amplitude of the focus signal also increases in amplitude nonlinearly as the loudspeaker volume is linearly increased. This nonlinear increase is the primary subject of investigation in this work. Experimental and computational methods are implemented in order to understand the mechanisms driving the nonlinear increases observed when the sources are combined acoustically as opposed to linear superposition of the contributions from each sound in post-processing. Finally, models of converging high-amplitude waves are generated using the k-Wave© package for MATLAB©. These show a similar nonlinear increase in amplitudes, supporting the hypothesis of a Mach wave coalescence. A COMSOL© finite element model allows visualization of the converging waves with Mach stems forming in free space to cause the nonlinear amplification.

The development of technologies to reduce turbofan engine noise reveals the fan noise, the first stage of an engine, as a great contributor for the total noise of an airplane. So a better understanding of the fan noise generation came up and motivated the construction of a fan rig test facility at the University of São Paulo in São Carlos by a partnership between the university and EMBRAER S.A.. The fan rig is composed of a long duct (12mlong) comprising a 16-bladed fan rotor and 14-vaned stator. The rotor is powered by an 100 hp electrical motor allowing speed up to 4250 RPM resulting in 0.1Mach axial flow. A 77-microphone wall-mounted array was designed for fan noise analysis. A cooperation with NASA-Glenn allowed data and information exchanging from their similar fan rig setup, the ANCF, grating then the validation of the in-house developed software. A short guide for duct-array is proposed in this work. Complex software was developed to process the data from the microphones array. We performed 3 different types of analysis: power spectral density, noise imaging obtained by acoustic beamforming and modal analysis.We proposed a different technique for modal analysis based on beamforming images in this work.We did not find any similar technique in the references. The results obtained by this technique were validated with data from ANCF-NASA. Comparative results are presented for both fan rigs, such as: power spectral densities for different fan speeds, modal analysis at the blade passing frequency (strong tones generated by the fan), noise imaging obtained by beamforming for rotating and static noise sources. Finally, results achieved in this work are in agreement with those observed in the references consulted. / Como desenvolver de tecnologias para redução de ruído de motores aeronáuticos turbofans, o ruído gerado pelo fan (primeiro estágio do motor) vem se mostrando cada vez mais um grande contribuinte na emissão total de ruído em um avião. Com isso, a necessidade de se estudar mecanismos geradores de ruído nestes motores veio à tona e motivou a construção de uma bancada de experimentos aero-acústicos junto àUniversidade de São Paulo, campus São Carlos, oriundo da parceria entre EMBRAER S.A. e Universidade de São Paulo. A bancada de ensaios compõe um conjunto rotor/estator, sendo que o fan (rotor) é equipado com 16 pás e a estatora 14 pás, conectado a um motor elétrico de 100 hp através de um eixo ao rotor, alcançando 4250 RPM com velocidade de escoamento axial médio de 0,1 Mach. Esta bancada é composta por um longo duto e a seção de ensaio com o fan localiza-se ao centro. Uma antena dispondo de 77 microfones foi especialmente projetada para fazer aquisição do ruído gerado pelo fan. Uma parceria com a NASA-Glenn possibilitou a troca de informações e dados experimentais de sua bancada de experimentos similar (ANCF) ajudando assim a validar os códigos desenvolvidos bem como comparar resultados para ambas as bancadas. Umpequeno roteiro para projeto de antena para análise modal e beamforming em duto é apresentado neste trabalho. Um complexo software foi desenvolvido a fim de processar sistematicamente os dados aquisitados pelos microfones da antena. Três tipos de análise são feitas: Via espectro densidade de potência; Imagem de ruído acústico obtido através da técnica de beamforming, e por último, análise modal. Uma técnica diferente para análise modal baseada em imagens obtidas através de beamforming é proposta neste trabalho. Nada similar foi encontrado nas referências consultadas. Os resultados foram validados com dados de fontes sintéticas produzidas pela bancada ANCF-NASA. Resultados comparativos para ambas as bancadas são exibidas neste trabalho, tais quais: Análise do espectro densidade de potência para diferentes rotações do fan; análise modal nas frequências de passagem das pás (forte ruído tonal gerado pelo fan); imagem acústica do ruído gerado tanto por fontes rotativas quanto para fontes estáticas. Finalmente, os resultados obtidos estão de acordo com o esperado e de antemão observados nas referências consultadas.

Air borne or liquid-laden solid particle transport is a common phenomenon in various industrial applications. Solid particles, transported at severe operating conditions such as high flow velocity, can cause concerns for structural integrity through wear originated from particle impacts with structure. To apply Acoustic Emission (AE) in particle impact monitoring, previous researchers focused primarily on dry particle impacts on dry target plate and/or wet particle impacts on wet or dry target plate. For dry particle impacts on dry target plate, AE events energy, calculated from the recorded free falling or air borne particle impact AE signals, were correlated with particle size, concentration, height, target material and thickness. For a given system, once calibrated for a specific particle type and operating condition, this technique might be sufficient to serve the purpose. However, if more than one particle type present in the system, particularly with similar size, density and impact velocity, calculated AE event energy is not unique for a specific particle type. For wet particle impacts on dry or wet target plate (either submerged or in a flow loop), AE event energy was related to the particle size, concentration, target material, impact velocity and angle between the nozzle and the target plate. In these studies, the experimental arrangements and the operating conditions considered either did not allow any bubble formation in the system or even if there is any at least an order of magnitude lower in amplitude than the sand particle impact and so easily identifiable. In reality, bubble formation can be comparable with particle impacts in terms of AE amplitude in process industries, for example, sand production during oil and gas transportation from reservoir. Current practice is to calibrate an installed AE monitoring system against a range of sand free flow conditions. In real time monitoring, for a specific calibrated flow, the flow generated AE amplitude/energy is deducted from the recorded AE amplitude/energy and the difference is attributed to the sand particle impacts. However, if the flow condition changes, which often does in the process industry, the calibration is not valid anymore and AE events from bubble can be misinterpreted as sand particle impacts and vice versa. In this research, sand particles and glass beads with similar size, density and impact velocity have been studied dropping from 200 mm on a small cylindrical stepped mild steel coupon as a target plate. For signal recording purposes, two identical broadband AE sensors are installed, one at the centre and one 30 mm off centred, on the opposite of the impacting surface. Signal analysis have been carried out by evaluating 7 standard AE parameters (amplitude, energy, rise time, duration, power spectral density(PSD), peak frequency at PSD and spectral centroid) in the time and frequency domain and time-frequency domain analysis have been performed applying Gabor Wavelet Transform. The signal interpretation becomes difficult due to reflections, dispersions and mode conversions caused by close proximity of the boundaries. So, a new signal analysis parameter - frequency band energy ratio - has been proposed. This technique is able to distinguish between population of two very similar groups (in terms of size and mass and energy) of sand particles and glass beads, impacting on mild steel based on the coefficient of variation (Cv) of the frequency band AE energy ratios. To facilitate individual particle impact identification, further analysis has been performed using Support Vector Machine (SVM) based classification algorithm using 7 standard AE parameters, evaluated in both the time and frequency domain. Available data set has been segmented into two parts of training set (80%) and test set (20%). The developed model has been applied on the test data for model performance evaluation purpose. The overall success rate of individually identifying each category (PLB, Glass bead and Sand particle impacts) at S1 has been found as 86% and at S2 as 92%. To study wet particle impacts on wet target surface, in presence of bubbles, the target plate has been sealed to a cylindrical perspex tube. Single and multiple sand particles have been introduced in the system using a constant speed blower to impact the target surface under water loading. Two sensor locations, used in the previous sets of experiments, have been monitored. From frequency domain analysis it has been observed that characteristic frequency for particle impacts are centred at 300-350 kHz and for bubble formations are centred at 135 – 150 kHz. Based upon this, two frequency bands 100 – 200 kHz (E1) and 300 – 400 kHz (E3) and the frequency band energy ratio (E3E1,) have been identified as optimal for identification particle impacts for the given system. E3E1, > 1 has been associated with particle impacts and E3E1, < 1 has been associated with bubble formations. Applying these frequency band energy ratios and setting an amplitude threshold, an automatic event identification technique has been developed for identification of sand particle impacts in presence of bubbles. The method developed can be used to optimize the identification of sand particle impacts. The optimal setting of an amplitude threshold is sensitive to number of particles and noise levels. A high threshold of say 10% will clearly identify sand particle impacts but for multiparticle tests is likely to not detect about 20% of lower (impact) energy particles. A threshold lower than 3% is likely to result in detection of AE events with poor frequency content and wrong classification of the weakest events. Optimal setting of the parameters used in the framework such as thresholds, frequency bands and ratios of AE energy is likely to make identification of sand particle impacts in the laboratory environment within 10% possible. For this technique, once the optimal frequency bands and ratios have been identified, then an added advantage is that calibration of the signal levels is not required.

The development of technologies to reduce turbofan engine noise reveals the fan noise, the first stage of an engine, as a great contributor for the total noise of an airplane. So a better understanding of the fan noise generation came up and motivated the construction of a fan rig test facility at the University of São Paulo in São Carlos by a partnership between the university and EMBRAER S.A.. The fan rig is composed of a long duct (12mlong) comprising a 16-bladed fan rotor and 14-vaned stator. The rotor is powered by an 100 hp electrical motor allowing speed up to 4250 RPM resulting in 0.1Mach axial flow. A 77-microphone wall-mounted array was designed for fan noise analysis. A cooperation with NASA-Glenn allowed data and information exchanging from their similar fan rig setup, the ANCF, grating then the validation of the in-house developed software. A short guide for duct-array is proposed in this work. Complex software was developed to process the data from the microphones array. We performed 3 different types of analysis: power spectral density, noise imaging obtained by acoustic beamforming and modal analysis.We proposed a different technique for modal analysis based on beamforming images in this work.We did not find any similar technique in the references. The results obtained by this technique were validated with data from ANCF-NASA. Comparative results are presented for both fan rigs, such as: power spectral densities for different fan speeds, modal analysis at the blade passing frequency (strong tones generated by the fan), noise imaging obtained by beamforming for rotating and static noise sources. Finally, results achieved in this work are in agreement with those observed in the references consulted. / Como desenvolver de tecnologias para redução de ruído de motores aeronáuticos turbofans, o ruído gerado pelo fan (primeiro estágio do motor) vem se mostrando cada vez mais um grande contribuinte na emissão total de ruído em um avião. Com isso, a necessidade de se estudar mecanismos geradores de ruído nestes motores veio à tona e motivou a construção de uma bancada de experimentos aero-acústicos junto àUniversidade de São Paulo, campus São Carlos, oriundo da parceria entre EMBRAER S.A. e Universidade de São Paulo. A bancada de ensaios compõe um conjunto rotor/estator, sendo que o fan (rotor) é equipado com 16 pás e a estatora 14 pás, conectado a um motor elétrico de 100 hp através de um eixo ao rotor, alcançando 4250 RPM com velocidade de escoamento axial médio de 0,1 Mach. Esta bancada é composta por um longo duto e a seção de ensaio com o fan localiza-se ao centro. Uma antena dispondo de 77 microfones foi especialmente projetada para fazer aquisição do ruído gerado pelo fan. Uma parceria com a NASA-Glenn possibilitou a troca de informações e dados experimentais de sua bancada de experimentos similar (ANCF) ajudando assim a validar os códigos desenvolvidos bem como comparar resultados para ambas as bancadas. Umpequeno roteiro para projeto de antena para análise modal e beamforming em duto é apresentado neste trabalho. Um complexo software foi desenvolvido a fim de processar sistematicamente os dados aquisitados pelos microfones da antena. Três tipos de análise são feitas: Via espectro densidade de potência; Imagem de ruído acústico obtido através da técnica de beamforming, e por último, análise modal. Uma técnica diferente para análise modal baseada em imagens obtidas através de beamforming é proposta neste trabalho. Nada similar foi encontrado nas referências consultadas. Os resultados foram validados com dados de fontes sintéticas produzidas pela bancada ANCF-NASA. Resultados comparativos para ambas as bancadas são exibidas neste trabalho, tais quais: Análise do espectro densidade de potência para diferentes rotações do fan; análise modal nas frequências de passagem das pás (forte ruído tonal gerado pelo fan); imagem acústica do ruído gerado tanto por fontes rotativas quanto para fontes estáticas. Finalmente, os resultados obtidos estão de acordo com o esperado e de antemão observados nas referências consultadas.

When recording voice samples from a patient in speech therapy the quality of the recording may be affected by different signal corruptions, for example background noise or clipping. The equipment and expertise required to identify small disturbances are not always present at smaller clinics. Therefore, this study investigates possible machine learning algorithms to automatically detect selected corruptions in speech signals, including infrasound and random muting. Five algorithms are analyzed: kernel substitution based Support Vector Machine, Convolutional Neural Network, Long Short-term Memory (LSTM), Gaussian Mixture Model based Hidden Markov Model and Generative Model based Hidden Markov Model. A tool to generate datasets of corrupted recordings is developed to test the algorithms in both single-label and multi-label settings. Mel-frequency Cepstral Coefficients are used as the main features. For each type of corruption different ways to increase the classification accuracy are tested, for example by using a Voice Activity Detector to filter out less relevant parts of the recording, changing the feature parameters, or using an ensemble of classifiers. The experiments show that a machine learning approach is feasible for this problem as a balanced accuracy of at least 75% is reached on all tested corruptions. While the single-label study gave mixed results with no algorithm clearly outperforming the others, in the multi-label case the LSTM in general performs better than other algorithms. Notably it achieves over 95% balanced accuracy on both white noise and infrasound. As the algorithms are trained only on spoken English phrases the usability of this tool in its current state is limited, but the experiments are easily expanded upon with other types of audio recordings, corruptions, features, or classification algorithms. / När en patients röst spelas in för analys i talterapi kan inspelningskvaliteten påverkas av olika signalproblem, till exempel bakgrundsljud eller klippning. Utrustningen och expertisen som behövs för att upptäcka små störningar finns dock inte alltid tillgänglig på mindre kliniker. Därför undersöker denna studie olika maskininlärningsalgoritmer för att automatiskt kunna upptäcka utvalda problem i talinspelningar, bland andra infraljud och slumpmässig utsläckning av signalen. Fem algoritmer analyseras: stödvektormaskin, Convolutional Neural Network, Long Short-term Memory (LSTM), Gaussian mixture model-baserad dold Markovmodell och generatorbaserad dold Markovmodell. Ett verktyg för att skapa datamängder med försämrade inspelningar utvecklas för att kunna testa algoritmerna. Vi undersöker separat fallen där inspelningarna tillåts ha en eller flera problem samtidigt, och använder framförallt en slags kepstralkoefficienter, MFCC:er, som särdrag. För varje typ av problem undersöker vi också sätt att förbättra noggrannheten, till exempel genom att filtrera bort irrelevanta delar av signalen med hjälp av en röstupptäckare, ändra särdragsparametrarna, eller genom att använda en ensemble av klassificerare. Experimenten visar att maskininlärning är ett rimligt tillvägagångssätt för detta problem då den balanserade träffsäkerheten överskrider 75%för samtliga testade störningar. Den delen av studien som fokuserade på enproblemsinspelningar gav inga resultat som tydde på att en algoritm var klart bättre än de andra, men i flerproblemsfallet överträffade LSTM:en generellt övriga algoritmer. Värt att notera är att den nådde över 95 % balanserad träffsäkerhet på både vitt brus och infraljud. Eftersom algoritmerna enbart tränats på engelskspråkiga, talade meningar så har detta verktyg i nuläget begränsad praktisk användbarhet. Däremot är det lätt att utöka dessa experiment med andra typer av inspelningar, signalproblem, särdrag eller algoritmer.