Relevant bibliographies by topics / Acoustic identification

Academic literature on the topic 'Acoustic identification'

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Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Acoustic identification"

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Stearns, Scott Donaldson. "Acoustic window identification." Journal of the Acoustical Society of America 112, no. 5 (2002): 1744. http://dx.doi.org/10.1121/1.1526596.

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Deng, Jiang Hua, Jun Hong Dong, and Guang De Meng. "Sound Source Identification and Acoustic Contribution Analysis Using Nearfield Acoustic Holography." Advanced Materials Research 945-949 (June 2014): 717–24. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.717.

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The main goal of the present paper is to provide a method of source identification. Firstly, statistically optimal near-field acoustical holography (SONAH) techniques are applied to locate sound sources with the reflected sound field. In the presence of reflection plane parallel and perpendicular to the source plane, the incoming wave and reflected waves are separated based on the acoustic superposition principle and acoustic mirror image principle to satisfy the condition of the sound sources reconstruction using SONAH. Secondly, contribution of noise source to the special field point is analyzed and noise source ranking of interior panel groups are evaluated based the proposed three step acoustic contribution method. Finally, this method is verified experimentally.
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Coker, Cecil H., and David R. Fischell. "Acoustic direction identification system." Journal of the Acoustical Society of America 80, no. 5 (November 1986): 1566. http://dx.doi.org/10.1121/1.394304.

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Iwatsubo, Takuzo, Shozo Kawamura, and Masahito Kamada. "Identification of Acoustic-Vibratory System by Acoustic Measurement." Shock and Vibration 3, no. 1 (1996): 27–37. http://dx.doi.org/10.1155/1996/925970.

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A new method for reducing ill-conditioning in a class of identification problems is proposed. The key point of the method is that the identified vibration of the sound source is expressed as a superposition of vibration modes. The mathematical property of the coefficient matrix, the practical error expanding ratio, and the stochastic error expanding ratio are investigated in a numerical example. The mode-superposition method is shown to be an effective tool for acoustic-vibratory inverse analysis.
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Samet, A., M. A. Ben Souf, O. Bareille, M. N. Ichchou, T. Fakhfakh, and M. Haddar. "Structural Source Identification from Acoustic Measurements Using an Energetic Approach." Journal of Mechanics 34, no. 4 (May 15, 2017): 431–41. http://dx.doi.org/10.1017/jmech.2017.24.

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AbstractAn inverse energy method for the identification of the structural force in high frequency ranges from radiated noise measurements is presented in this paper. The radiation of acoustic energy of the structure coupled to an acoustic cavity is treated using an energetic method called the simplified energy method. The main novelty of this paper consists in using the same energy method to solve inverse structural problem. It consists of localization and quantification of the vibration source through the knowledge of acoustic energy density. Numerical test cases with different measurement points are used for validation purpose. The numerical results show that the proposed method has an excellent performance in detecting the structural force with a few acoustical measurements.
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Kloser, R. J., T. Ryan, P. Sakov, A. Williams, and J. A. Koslow. "Species identification in deep water using multiple acoustic frequencies." Canadian Journal of Fisheries and Aquatic Sciences 59, no. 6 (June 1, 2002): 1065–77. http://dx.doi.org/10.1139/f02-076.

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Multifrequency 12, 38, and 120 kHz acoustics were used to identify the dominant fish groups around a deepwater (>600 m) seamount (a known spawning site for orange roughy, Hoplostethus atlanticus) by amplitude mixing of the frequencies. This method showed three distinct acoustic groupings that corresponded to three groups of fishes based on size and swimbladder type: myctophids of total length less than 10 cm, morids and macrourids with lengths >30 cm, and orange roughy with a mean standard length of 36 cm. These three groups were the dominant groups caught in the demersal and pelagic trawls in the study area. A simple model of swimbladder resonance at depth of large and small gas-filled bladder fish groups is in agreement with our experimental observations. Traditionally, demersal and pelagic trawling is used to identify fish species in acoustic records. However, orange roughy are rarely caught in mid-water owing to net avoidance. Using three frequencies, these groups could be distinguished directly over their entire vertical extent from the acoustic records. This reduces a major source of positive bias uncertainty (factor range of 2.0–6.4) in the orange roughy biomass estimates.
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Potapov, A. I., I. S. Pavlov, and S. A. Lisina. "Acoustic identification of nanocrystalline media." Journal of Sound and Vibration 322, no. 3 (May 2009): 564–80. http://dx.doi.org/10.1016/j.jsv.2008.09.031.

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Korneliussen, Rolf J., Yngve Heggelund, Inge K. Eliassen, and Geir O. Johansen. "Acoustic species identification of schooling fish." ICES Journal of Marine Science 66, no. 6 (May 2, 2009): 1111–18. http://dx.doi.org/10.1093/icesjms/fsp119.

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Abstract Korneliussen, R. J., Heggelund, Y., Eliassen, I. K., and Johansen, G. O. 2009. Acoustic species identification of schooling fish. – ICES Journal of Marine Science, 66: 1111–1118. The development of methods for the acoustic identification of fish is a long-term objective aimed at reducing uncertainty in acoustic-survey estimates. The relative frequency response r(f) measured simultaneously at several frequencies is one of the main acoustic features that characterize the targets, but the relationship between nearest neighbours, school morphology, and environmental and geographical data are also important characteristics in this context. The number of acoustic categories that can be separated with a high spatial resolution is limited by the stochastic nature of the measurements. Because the acoustic categorization of larger ensembles is more reliable than for single targets, spatial smoothing of the backscattering within the school boundaries before that process allows the separation of more categories than is possible with the raw, highly resolved data. Using the mean r(f) of an entire school gives even more reliable categorization, but determining whether or not the school is monospecific sets a new challenge. This problem is evaluated here. The methods are tested and verified. Identification of acoustic categories with similar acoustic properties is done for schooling fish, although the results have limited spatial resolution. The reliability of the categorization is further improved when knowledge of school morphology and geographical distribution of the species are taken into account.
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Korneliussen, Rolf J. "The acoustic identification of Atlantic mackerel." ICES Journal of Marine Science 67, no. 8 (June 8, 2010): 1749–58. http://dx.doi.org/10.1093/icesjms/fsq052.

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Abstract Korneliussen, R. J. 2010. The acoustic identification of Atlantic mackerel. – ICES Journal of Marine Science, 67: 1749–1758. Calibrated, digitized data from multifrequency echosounders working simultaneously with nearly identical and overlapping acoustic beams were used to generate new, synthetic echograms which allow Atlantic mackerel (Scomber scombrus) to be identified acoustically. The raw echosounder data were processed stepwise in a modular sequence of analyses to improve categorization of the acoustic targets. The relative frequency response measured over as many as six operating frequencies, 18, 38, 70, 120, 200, and 364 kHz, was the main acoustic feature used to characterize the backscatter. Mackerel seemed to have a frequency-independent backscatter below ∼100 kHz, but significantly higher levels of backscattered energy at 200 kHz. Synthetic echograms containing targets identified acoustically as mackerel are presented and evaluated against trawl catches. Although catching fast-swimming mackerel is difficult, trawl catches from three Norwegian research vessels confirmed that the targets identified acoustically as mackerel were indeed that species. Separate experiments performed on mackerel in pens support the findings.
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Gomez Morales, J., R. Rodriguez, J. Durand, H. Ferdj-Allah, Z. Hadjoub, J. Attal, and A. Doghmane. "Characterization and identification of berlinite crystals by acoustic microscopy." Journal of Materials Research 6, no. 11 (November 1991): 2484–89. http://dx.doi.org/10.1557/jmr.1991.2484.

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Berlinite crystals grown in H3PO4, HCl, H3PO4/HCl, H2SO4/HCl, or H3PO4/HCl/H2SO4 solvents are characterized by acoustic microscopy techniques. Surface and subsurface defects can be visualized via acoustical images, whereas elastic parameters of the crystal can be measured on a microscopic scale. They prove to be of great importance in the identification of not only crystal orientations but of preparation methods as well. We show, for example, that a growth in sulfuric and phosphoric mediums improves mechanical behavior of berlinite crystals. Moreover, it seems that anisotropy plays a fundamental role in this characterization technique with an appearance or a disappearance of specific modes.
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Dissertations / Theses on the topic "Acoustic identification"

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Silva, Bruno Miguel Santos Antunes. "Automated acoustic identification of bat species." Master's thesis, Universidade de Évora, 2013. http://hdl.handle.net/10174/9101.

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Automated acoustic identification of bat species Recent improvements in bat survey methods in Portugal, especially automatic recording stations, have led to an analysis problem due to the amount of data obtained. In this thesis we propose to develop an automated analysis and classification method for bat echolocation calls by developing a computer program based on statistical models and using a reference database of bat calls recorded in Portugal to quickly analyze and classify large amounts of recordings. We recorded 2968 calls from 748 bats of 20 (of the 25) bat species known in mainland Portugal and coded a program in R that automatically detects bat calls in a recording, isolates the calls from the background noise and measures 19 parameters from each call. A two stage hierarchical classification bat call scheme was implemented based on logistic regression models and ensembles of artificial neural networks. In the first stage calls were classified in six major groups with individual correct classification rates that varied between 93% and 100%. In the second stage calls were classified in species or groups of species with classification rates that varied between 50% and 100%; ### Identificação acústica automatizada de espécies de morcegos Desenvolvimentos recentes nas metodologias de monitorização de morcegos utilizadas em Portugal, especialmente estações de gravação automáticas, conduziram a um problema de análise devido à quantidade de dados obtida. Nesta tese propomos desenvolver um método automatizado de análise e classificação de pulsos de ecolocalização de morcegos através do desenvolvimento de um programa de computador baseado em modelos estatísticos e utilizando uma base de dados de pulsos de morcegos gravados em Portugal continental para rapidamente analisar e classificar grandes quantidades de gravações. Gravámos 2968 pulsos de 748 morcegos de 20 (das 25) espécies de morcegos conhecidas em Portugal continental e codificámos em R um programa para automaticamente detectar pulsos de morcego numa gravação, isolar os pulsos do ruído de fundo e medir 19 parâmetros de cada pulso. Foi implementado um esquema hierárquico de classificação de pulsos em duas etapas baseado em modelos de regressão logística e conjuntos de redes neuronais artificiais. Numa primeira etapa os pulsos foram classificados em seis grupos com taxas individuais de classificações correctas que variaram entre 93% e 100%. Numa segunda fase os pulsos foram classificados em espécies ou grupos de espécies com taxas de classificação correctas que variaram entre 50% e 100%.
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Cabell, Randolph H. "The automatic identification of aerospace acoustic sources." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/45932.

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This work describes the design of an intelligent recognition system used to distinguish noise signatures of five different acoustic sources. The system uses pattern recognition techniques to identify the information obtained from a single microphone. A training phase is used in which the system learns to distinguish the sources and automatically selects features for optimal performance. Results were obtained by training the system to distinguish jet planes, propeller planes, a helicopter, train, and wind turbine from one another, then presenting similar sources to the system and recording the number of errors. These results indicate the system can successfully identify the trained sources based on acoustic information. Classification errors highlight the impact of the training sources on the system's ability to recognize different sources.


Master of Science
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DeMarco, Andrea. "Acoustic approaches to gender and accent identification." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/53443/.

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There has been considerable research on the problems of speaker and language recognition from samples of speech. A less researched problem is that of accent recognition. Although this is a similar problem to language identification, different accents of a language exhibit more fine-grained differences between classes than languages. This presents a tougher problem for traditional classification techniques. In this thesis, we propose and evaluate a number of techniques for gender and accent classification. These techniques are novel modifications and extensions to state of the art algorithms, and they result in enhanced performance on gender and accent recognition. The first part of the thesis focuses on the problem of gender identification, and presents a technique that gives improved performance in situations where training and test conditions are mismatched. The bulk of this thesis is concerned with the application of the i-Vector technique to accent identification, which is the most successful approach to acoustic classification to have emerged in recent years. We show that it is possible to achieve high accuracy accent identification without reliance on transcriptions and without utilising phoneme recognition algorithms. The thesis describes various stages in the development of i-Vector based accent classification that improve the standard approaches usually applied for speaker or language identification, which are insufficient. We demonstrate that very good accent identification performance is possible with acoustic methods by considering different i-Vector projections, frontend parameters, i-Vector configuration parameters, and an optimised fusion of the resulting i-Vector classifiers we can obtain from the same data. We claim to have achieved the best accent identification performance on the test corpus for acoustic methods, with up to 90% identification rate. This performance is even better than previously reported acoustic-phonotactic based systems on the same corpus, and is very close to performance obtained via transcription based accent identification. Finally, we demonstrate that the utilization of our techniques for speech recognition purposes leads to considerably lower word error rates.
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Fox, Elizabeth J. S. "Call-independent identification in birds." University of Western Australia. School of Animal Biology, 2008. http://theses.library.uwa.edu.au/adt-WU2008.0218.

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[Truncated abstract] The identification of individual animals based on acoustic parameters is a non-invasive method of identifying individuals with considerable advantages over physical marking procedures. One requirement for an effective and practical method of acoustic individual identification is that it is call-independent, i.e. determining identity does not require a comparison of the same call or song type. This means that an individuals identity over time can be determined regardless of any changes to its vocal repertoire, and different individuals can be compared regardless of whether they share calls. Although several methods of acoustic identification currently exist, for example discriminant function analysis or spectrographic cross-correlation, none are call-independent. Call-independent identification has been developed for human speaker recognition, and this thesis aimed to: 1) determine if call-independent identification was possible in birds, using similar methods to those used for human speaker recognition, 2) examine the impact of noise in a recording on the identification accuracy and determine methods of removing the noise and increasing accuracy, 3) provide a comparison of features and classifiers to determine the best method of call-independent identification in birds, and 4) determine the practical limitations of call-independent identification in birds, with respect to increasing population size, changing vocal characteristics over time, using different call categories, and using the method in an open population. ... For classification, Gaussian mixture models and probabilistic neural networks resulted in higher accuracy, and were simpler to use, than multilayer perceptrons. Using the best methods of feature extraction and classification resulted in 86-95.5% identification accuracy for two passerine species, with all individuals correctly identified. A study of the limitations of the technique, in terms of population size, the category of call used, accuracy over time, and the effects of having an open population, found that acoustic identification using perceptual linear prediction and probabilistic neural networks can be used to successfully identify individuals in a population of at least 40 individuals, can be used successfully on call categories other than song, and can be used in open populations in which a new recording may belong to a previously unknown individual. However, identity was only able to be determined with accuracy for less than three months, limiting the current technique to short-term field studies. This thesis demonstrates the application of speaker recognition technology to enable call-independent identification in birds. Call-independence is a pre-requisite for the successful application of acoustic individual identification in many species, especially passerines, but has so far received little attention in the scientific literature. This thesis demonstrates that call-independent identification is possible in birds, as well as testing and finding methods to overcome the practical limitations of the methods, enabling their future use in biological studies, particularly for the conservation of threatened species.
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Schuler, Leo Pius. "Wireless identification and sensing using surface acoustic wave devices." Thesis, University of Canterbury. Electrical Engineering, 2003. http://hdl.handle.net/10092/1081.

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Wireless Surface Acoustic Wave (SAW) devices were fabricated and tested using planar Lithium Niobate (LiNbO₃) as substrate. The working frequencies were in the 180 MHz and 360 MHz range. Using a network analyser, the devices were interrogated with a wireless range of more than 2 metres. Trials with Electron Beam Lithography (EBL) to fabricate SAW devices working in the 2450 MHz with a calculated feature size of 350 nm are discussed. Charging problems became evident as LiNbO₃ is a strong piezoelectric and pyroelectric material. Various attempts were undertaken to neutralise the charging problems. Further investigation revealed that sputtered Zinc Oxide (ZnO) is a suitable material for attaching SAW devices on irregularly shaped material. DC sputtering was used and several parameters have been optimised to achieve the desired piezoelectric effect. ZnO was sputtered using a magnetron sputtering system with a 75 mm Zn target and a DC sputter power of 250 Watts. Several trials were performed and an optimised material has been prepared under the following conditions: 9 sccm of Oxygen and 6 sccm of Argon were introduced during the process which resulted in a process pressure of 1.2x10⁻² mbar. The coatings have been characterised using Rutherford Backscattering, X-ray diffraction, SEM imaging, and Atomic force microscopy. SAW devices were fabricated and tested on 600 nm thick sputtered ZnO on a Si substrate with a working frequency of 430 MHz. The phase velocity has been calculated as 4300m/s. Non-planar samples have been coated with 500 nm of sputtered ZnO and SAW structures have been fabricated on using EBL. The design frequency is 2450 MHz, with a calculated feature size of 1 µm. The surface roughness however prevented a successful lift-off. AFM imaging confirmed a surface roughness in the order of 20 nm. Ways to improve manufacturability on these samples have been identified.
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Schuler, Leo P. "Wireless identification and sensing using surface acoustic wave devices." Thesis, University of Canterbury. Engineering, 2003. http://hdl.handle.net/10092/8565.

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Wireless Surface Acoustic Wave (SAW) devices were fabricated and tested using planar Lithium Niobate (LiNbO₃) as substrate. The working frequencies were in the 180 MHz and 360 MHz range. Using a network analyser, the devices were interrogated with a wireless range of more than 2 metres. Trials with Electron Beam Lithography (EBL) to fabricate SAW devices working in the 2450 MHz with a calculated feature size of 350 nm are discussed. Charging problems became evident as LiNbO₃ is a strong piezoelectric and pyroelectric material. Various attempts were undertaken to neutralise the charging problems. Further investigation revealed that sputtered Zinc Oxide (ZnO) is a suitable material for attaching SAW devices on irregularly shaped material. DC sputtering was used and several parameters have been optimised to achieve the desired piezoelectric effect. ZnO was sputtered using a magnetron sputtering system with a 75 mm Zn target and a DC sputter power of 250 Watts. Several trials were performed and an optimised material has been prepared under the following conditions: 9 sccm of Oxygen and 6 seem of Argon were introduced during the process which resulted in a process pressure of 1.2x10⁻² mbar. The coatings have been characterised using Rutherford Backscattering, X-ray diffraction, SEM imaging, and Atomic force microscopy. SAW devices were fabricated and tested on 600 nm thick sputtered ZnO on a Si substrate with a working frequency of 430 MHz. The phase velocity has been calculated as 4300m/s. Non-planar samples have been coated with 500 nm of sputtered ZnO and SAW structures have been fabricated on using BBL. The design frequency is 2450 MHz, with a calculated feature size of 1 μm. The surface roughness however prevented a successful lift-off. AFM imaging confirmed a surface roughness in the order of 20 nm. Ways to improve manufacturability on these samples have been identified.
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Hedayetullah, Amin Mohammad. "Optimization of identification of particle impacts using acoustic emission." Thesis, Robert Gordon University, 2018. http://hdl.handle.net/10059/3116.

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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.
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Schofield, James. "Real-time acoustic identification of invasive wood-boring beetles." Thesis, University of York, 2011. http://etheses.whiterose.ac.uk/1978/.

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Wood-boring beetles are a cause of significant economic and environmental cost across the world. A number of species which are not currently found in the United Kingdom are constantly at risk of being accidentally imported due to the volume of global trade in trees and timber. The species which are of particular concern are the Asian Longhorn (Anoplophora glabripennis), Citrus Longhorn (A. chinensis) and Emerald Ash Borer (Agrilus planipennis). The Food and Environment Research Agency's plant health inspectors currently manually inspect high risk material at the point of import. The development of methods which will enable them to increase the probability of detection of infestation in imported material are therefore highly sought after. This thesis describes research into improving acoustic larvae detection and species identification methods, and the development of a real-time system incorporating them. The detection algorithm is based upon fractal dimension analysis and has been shown to outperform previously used short-time energy based detection. This is the first time such a detection method has been applied to the analysis of insect sourced sounds. The species identification method combines a time domain feature extraction technique based upon the relational tree representation of discrete waveforms and classification using artificial neural networks. Classification between two species, A. glabripennis and H. bajulus, can be performed with 92% accuracy using Multilayer Perceptron and 96.5% accuracy using Linear Vector Quantisation networks. Classification between three species can be performed with 88.8% accuracy using LVQ. A real-time hand-held PC based system incorporating these methods has been developed and supplied to FERA for further testing. This system uses a combination of dual piezo-electric based USB connected sensors and custom written software which can be used to analyse live recordings of larvae in real-time or use previously recorded data.
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Moat, Trevor P. B. M. "Orthogonal adaptive digital filters with applications to acoustic system identification." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape17/PQDD_0025/MQ27022.pdf.

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Gaubitch, Nikolay Dian. "Blind identification of acoustic systems and enhancement of reverberant speech." Thesis, Imperial College London, 2007. http://hdl.handle.net/10044/1/12025.

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Books on the topic "Acoustic identification"

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Helwani, Karim. Adaptive Identification of Acoustic Multichannel Systems Using Sparse Representations. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08954-6.

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Banks, H. Thomas. Parameter estimation in a structural acoustic system with fully nonlinear coupling conditions. Hampton, Va: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1994.

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Farren, Maureen A. Some experiments with underwater acoustic returns from cylinders relative to object identification for AUV operation. Monterey, California: Naval Postgraduate School, 1988.

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Healey, Anthony J. Sonar signal acquisition and processing for identification and classification of ship hull fouling. Monterey, Calif: Naval Postgraduate School, 1993.

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Hanson, David R. Multiple-channel trigger circuit for noise discrimination in ultrasonic acoustic emission studies. Washington, DC: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Hanson, David R. Multiple-channel trigger circuit for noise discrimination in ultrasonic acoustic emission studies. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Hanson, David R. Multiple-channel trigger circuit for noise discrimination in ultrasonic acoustic emission studies. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Hanson, David R. Multiple-channel trigger circuit for noise discrimination in ultrasonic acoustic emission studies. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Hanson, David R. Multiple-channel trigger circuit for noise discrimination in ultrasonic acoustic emission studies. [Washington, D.C.?]: U.S. Dept. of the Interior, Bureau of Mines, 1995.

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Fuller, C. R. Application of pattern recognition techniques to the identification of aerospace acoustic sources: Annual report, year one. [Washington, DC: National Aeronautics and Space Administration, 1988.

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Book chapters on the topic "Acoustic identification"

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Sas, P. "Numerical Acoustic Radiation Models." In Application of System Identification in Engineering, 233–50. Vienna: Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-2628-8_4.

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Sas, P. "Digital Acoustic Intensity Measurements." In Application of System Identification in Engineering, 251–78. Vienna: Springer Vienna, 1988. http://dx.doi.org/10.1007/978-3-7091-2628-8_5.

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Miles, Ronald N. "Parameter Identification of Acoustic Systems." In Mechanical Engineering Series, 331–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22676-3_13.

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Tacconi, Giorgio, and Antonio Tiano. "Applied Modelling to Underwater Vehicles Identification." In Underwater Acoustic Data Processing, 413–19. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_45.

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Castagnède, Bernard. "Acoustic Emission Source Location in Anisotropic Composite Plates." In Mechanical Identification of Composites, 433–41. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3658-7_49.

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Narkhede, Meenal, and Rashmika Patole. "Acoustic Scene Identification for Audio Authentication." In Advances in Intelligent Systems and Computing, 593–602. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-3600-3_56.

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Munier, J., G. Jourdain, and G. Y. Delisle. "A New Algorithm for the Identification of Distorted Wavefronts." In Underwater Acoustic Data Processing, 87–91. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_7.

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Benesty, Jacob, Constantin Paleologu, Tomas Gänsler, and Silviu Ciochină. "System Identification with the Wiener Filter." In A Perspective on Stereophonic Acoustic Echo Cancellation, 13–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22574-1_3.

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Ziola, Steve, and Ian Searle. "Automated Source Identification Using Modal Acoustic Emission." In Review of Progress in Quantitative Nondestructive Evaluation, 413–19. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-5947-4_55.

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Boone, Marinus M. "Design and Development of an Acoustic Antenna System for Industrial Noise Source Identification." In Underwater Acoustic Data Processing, 379–84. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_42.

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Conference papers on the topic "Acoustic identification"

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Cevher, V., and J. H. McClellan. "An acoustic multiple target tracker." In 2005 Microwave Electronics: Measurements, Identification, Applications. IEEE, 2005. http://dx.doi.org/10.1109/ssp.2005.1628648.

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Sharma, Manish, and Richard J. Mammone. "Neural tree network for speech segmentation into subword acoustic units." In Substance Identification Technologies, edited by James L. Flanagan, Richard J. Mammone, Albert E. Brandenstein, Edward R. Pike, Stelios C. A. Thomopoulos, Marie-Paule Boyer, H. K. Huang, and Osman M. Ratib. SPIE, 1994. http://dx.doi.org/10.1117/12.172489.

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Rusovici, Razvan, and Daniel Mason. "Coupled Acoustic-Structural-Piezoelectric Modeling of Synthetic Jet." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2014. http://dx.doi.org/10.2316/p.2014.809-064.

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Calkins, Luke, Reza Khodayi-mehr, Wilkins Aquino, and Michael M. Zavlanos. "Physics-Based Acoustic Source Identification." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619483.

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Dumbacher, Susan, Jason Blough, Darren Hallman, and Percy Wang. "Source Identification Using Acoustic Array Techniques." In SAE Noise and Vibration Conference and Exposition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1995. http://dx.doi.org/10.4271/951360.

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Bingham, B., D. Mindell, D. Yoerger, B. Foley, and W. Seering. "Acoustic multipath identification with expectation-maximization." In Oceans 2003. Celebrating the Past ... Teaming Toward the Future (IEEE Cat. No.03CH37492). IEEE, 2003. http://dx.doi.org/10.1109/oceans.2003.178287.

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Malik, Hafiz, and Hong Zhao. "Recording environment identification using acoustic reverberation." In ICASSP 2012 - 2012 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2012. http://dx.doi.org/10.1109/icassp.2012.6288258.

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Karasalo, I., and P. Skogqvist. "Object identification by bistatic acoustic scattering." In Oceans 2005 - Europe. IEEE, 2005. http://dx.doi.org/10.1109/oceanse.2005.1511764.

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Biernacki, Pawel. "Acoustic information fusion for vehicles identification." In 2014 19th International Conference on Methods & Models in Automation & Robotics (MMAR). IEEE, 2014. http://dx.doi.org/10.1109/mmar.2014.6957441.

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Lee, H. S., J. S. Won, K. S. Park, M. C. Shin, D. Y. Sun, S. Hur, and E. J. Lee. "Acoustic target impact point identification system." In 2017 4th International Conference on Systems and Informatics (ICSAI). IEEE, 2017. http://dx.doi.org/10.1109/icsai.2017.8248444.

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Reports on the topic "Acoustic identification"

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Cobb, Wesley. Acoustic Identification of Filler Materials in Unexploded Ordnance. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada468491.

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Roch, Marie A. Passive Acoustic Monitoring for the Detection and Identification of Marine Mammals. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada541770.

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Cobb, Wesley. Operational Evaluation of a New Acoustic Technique for UXO Filler Identification. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada520496.

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Cobb, Wes. Operational Evaluation of a New Acoustic Technique for UXO Filler Identification. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada520499.

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Perry, R. L., and R. S. Roberts. In-situ identification of anti-personnel mines using acoustic resonant spectroscopy. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/8430.

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Bucaro, J. A., B. H. Houston, H. Simpson, Z. Waters, M. Saniga, S. Dey, A. Sarkissian, D. Calvo, L. Kraus, and T. Yoder. Wide Area Detection and Identification of Underwater UXO Using Structural Acoustic Sensors. Fort Belvoir, VA: Defense Technical Information Center, July 2011. http://dx.doi.org/10.21236/ada546324.

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Hedlin, Michael A. Regional Small-Event Identification Using Networks and Arrays of Seismic and Acoustic Sensors. Fort Belvoir, VA: Defense Technical Information Center, April 2006. http://dx.doi.org/10.21236/ada455277.

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Sinha, D. N., K. Springer, W. Han, D. Lizon, and S. Kogan. Applications of swept-frequency acoustic interferometer for nonintrusive detection and identification of chemical warfare compounds. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/555542.

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D'Amico, Angela, Christopher Kyburg, and Rowena Carlson. Software Tools for Visual and Acoustic Real-Time Tracking of Marine Mammals: Whale Identification and Logging Display (WILD). Fort Belvoir, VA: Defense Technical Information Center, November 2010. http://dx.doi.org/10.21236/ada533470.

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Bucaro, Joseph A., Brian H. Houston, Harry Simpson, Michael Saniga, Angie Sarkissian, D. Calvo, L. Kraus, and T. Yoder. Wide Area Detection and Identification of Underwater UXO Using Structural Acoustic Senors: 4th Annual Report to SERDP MM-1513. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada525163.

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