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Martin, Traykovski Linda V. (Linda Victoria) 1966. "Acoustic classification of zooplankton." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/49620.
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Temko, Andriy. "Acoustic event detection and classification." Doctoral thesis, Universitat Politècnica de Catalunya, 2007. http://hdl.handle.net/10803/6880.
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L'activitat humana que té lloc en sales de reunions o aules d'ensenyament es veu reflectida en una rica varietat d'events acústics, ja siguin produïts pel cos humà o per objectes que les persones manegen. Per això, la determinació de la identitat dels sons i de la seva posició temporal pot ajudar a detectar i a descriure l'activitat humana que té lloc en la sala. A més a més, la detecció de sons diferents de la veu pot ajudar a millorar la robustes de tecnologies de la parla com el reconeixement automàtica a condicions de treball adverses. L'objectiu d'aquesta tesi és la detecció i classificació automàtica d'events acústics. Es tracta de processar els senyals acústics recollits per micròfons distants en sales de reunions o aules per tal de convertir-los en descripcions simbòliques que es corresponguin amb la percepció que un oient tindria dels diversos events sonors continguts en els senyals i de les seves fonts. En primer lloc, s'encara la tasca de classificació automàtica d'events acústics amb classificadors de màquines de vectors suport (Support Vector Machines (SVM)), elecció motivada per l'escassetat de dades d'entrenament. Per al problema de reconeixement multiclasse es desenvolupa un esquema d'agrupament automàtic amb conjunt de característiques variable i basat en matrius de confusió. Realitzant proves amb la base de dades recollida, aquest classificador obté uns millors resultats que la tècnica basada en models de barreges de Gaussianes (Gaussian Mixture Models (GMM)), i aconsegueix una reducció relativa de l'error mitjà elevada en comparació amb el millor resultat obtingut amb l'esquema convencional basat en arbre binari. Continuant amb el problema de classificació, es comparen unes quantes maneres alternatives d'estendre els SVM al processament de seqüències, en un intent d'evitar l'inconvenient de treballar amb vectors de longitud fixa que presenten els SVM quan han de tractar dades d'àudio. En aquestes proves s'observa que els nuclis de deformació temporal dinàmica funcionen bé amb sons que presenten una estructura temporal. A més a més, s'usen conceptes i eines manllevats de la teoria de lògica difusa per investigar, d'una banda, la importància de cada una de les característiques i el grau d'interacció entre elles, i d'altra banda, tot cercant l'augment de la taxa de classificació, s'investiga la fusió de lessortides de diversos sistemes de classificació. Els sistemes de classificació d'events acústics
desenvolupats s'han testejat també mitjançant la participació en unes quantes avaluacions d'àmbit
internacional, entre els anys 2004 i 2006. La segona principal contribució d'aquest treball de tesi consisteix en el desenvolupament de sistemes de detecció d'events acústics. El problema de la detecció és més complex, ja que inclou tant la classificació dels sons com la determinació dels intervals temporals on tenen lloc. Es desenvolupen dues versions del sistema i es proven amb els conjunts de dades de les dues campanyes d'avaluació internacional CLEAR que van tenir lloc els anys 2006 i 2007, fent-se servir dos tipus de bases de dades: dues bases d'events acústics aïllats, i una base d'enregistraments de seminaris interactius, les quals contenen un nombre relativament elevat d'ocurrències dels events acústics especificats. Els sistemes desenvolupats, que consisteixen en l'ús de classificadors basats en SVM que operen dins
d'una finestra lliscant més un post-processament, van ser els únics presentats a les avaluacions
esmentades que no es basaven en models de Markov ocults (Hidden Markov Models) i cada un d'ells
va obtenir resultats competitius en la corresponent avaluació. La detecció d'activitat oral és un altre dels objectius d'aquest treball de tesi, pel fet de ser un cas particular de detecció d'events acústics especialment important. Es desenvolupa una tècnica de millora de l'entrenament dels SVM per fer front a la necessitat de reducció de l'enorme conjunt de dades existents. El sistema resultant, basat en SVM, és testejat amb uns quants conjunts de dades de l'avaluació NIST RT (Rich Transcription), on mostra puntuacions millors que les del sistema basat en GMM, malgrat que aquest darrer va quedar entre els primers en l'avaluació NIST RT de 2006.
Per acabar, val la pena esmentar alguns resultats col·laterals d'aquest treball de tesi. Com que s'ha dut a terme en l'entorn del projecte europeu CHIL, l'autor ha estat responsable de l'organització de les avaluacions internacionals de classificació i detecció d'events acústics abans esmentades, liderant l'especificació de les classes d'events, les bases de dades, els protocols d'avaluació i, especialment, proposant i implementant les diverses mètriques utilitzades. A més a més, els sistemes de detecció
s'han implementat en la sala intel·ligent de la UPC, on funcionen en temps real a efectes de test i demostració.
The human activity that takes place in meeting-rooms or class-rooms is reflected in a rich variety of acoustic events, either produced by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity.
Additionally, detection of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition. Automatic detection and classification of acoustic events is the objective of this thesis work. It aims at processing the acoustic signals collected by distant microphones in meeting-room or classroom environments to convert them into symbolic descriptions corresponding to a listener's perception of the different sound events that are present in the signals and their sources. First of all, the task of acoustic event classification is faced using Support Vector Machine (SVM) classifiers, which are motivated by the scarcity of training data. A confusion-matrix-based variable-feature-set clustering scheme is developed for the multiclass recognition problem, and tested on the gathered database. With it, a higher classification rate than the GMM-based technique is obtained, arriving to a large relative average error reduction with respect to the best result from the conventional binary tree scheme. Moreover, several ways to extend SVMs to sequence processing are compared, in an attempt to avoid the drawback of SVMs when dealing with audio data, i.e. their restriction to work with fixed-length vectors, observing that the dynamic time warping kernels work well for sounds that show a temporal structure. Furthermore, concepts and tools from the fuzzy theory are used to investigate, first, the importance of and degree of interaction among features, and second, ways to fuse the outputs of several classification systems. The developed AEC systems are tested also by participating in several international evaluations from 2004 to 2006, and the results
are reported. The second main contribution of this thesis work is the development of systems for detection of acoustic events. The detection problem is more complex since it includes both classification and determination of the time intervals where the sound takes place. Two system versions are developed and tested on the datasets of the two CLEAR international evaluation campaigns in 2006 and 2007. Two kinds of databases are used: two databases of isolated acoustic events, and a database of interactive seminars containing a significant number of acoustic events of interest. Our developed systems, which consist of SVM-based classification within a sliding window plus post-processing, were the only submissions not using HMMs, and each of them obtained competitive results in the corresponding evaluation. Speech activity detection was also pursued in this thesis since, in fact, it is a -especially important - particular case of acoustic event detection. An enhanced SVM training approach for the speech activity detection task is developed, mainly to cope with the problem of dataset reduction. The resulting SVM-based system is tested with several NIST Rich Transcription (RT) evaluation datasets, and it shows better scores than our GMM-based system, which ranked among the best systems in the RT06 evaluation. Finally, it is worth mentioning a few side outcomes from this thesis work. As it has been carried out in the framework of the CHIL EU project, the author has been responsible for the organization of the above mentioned international evaluations in acoustic event classification and detection, taking a leading role in the specification of acoustic event classes, databases, and evaluation protocols, and, especially, in the proposal and implementation of the various metrics that have been used. Moreover, the detection systems have been implemented in the UPC's smart-room and work in real time for purposes of testing and demonstration.
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Brock, James L. "Acoustic classification using independent component analysis /." Link to online version, 2006. https://ritdml.rit.edu/dspace/handle/1850/2067.
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Caughey, David Arthur. "Seabed classification from acoustic echosounder returns." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp02/NQ32738.pdf.
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Hassan, Ali. "On automatic emotion classification using acoustic features." Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/340672/.
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In this thesis, we describe extensive experiments on the classification of emotions from speech using acoustic features. This area of research has important applications in human computer interaction. We have thoroughly reviewed the current literature and present our results on some of the contemporary emotional speech databases. The principal focus is on creating a large set of acoustic features, descriptive of different emotional states and finding methods for selecting a subset of best performing features by using feature selection methods. In this thesis we have looked at several traditional feature selection methods and propose a novel scheme which employs a preferential Borda voting strategy for ranking features. The comparative results show that our proposed scheme can strike a balance between accurate but computationally intensive wrapper methods and less accurate but computationally less intensive filter methods for feature selection. By using the selected features, several schemes for extending the binary classifiers to multiclass classification are tested. Some of these classifiers form serial combinations of binary classifiers while others use a hierarchical structure to perform this task. We describe a new hierarchical classification scheme, which we call Data-Driven Dimensional Emotion Classification (3DEC), whose decision hierarchy is based on non-metric multidimensional scaling (NMDS) of the data. This method of creating a hierarchical structure for the classification of emotion classes gives significant improvements over other methods tested. The NMDS representation of emotional speech data can be interpreted in terms of the well-known valence-arousal model of emotion. We find that this model does not give a particularly good fit to the data: although the arousal dimension can be identified easily, valence is not well represented in the transformed data. From the recognition results on these two dimensions, we conclude that valence and arousal dimensions are not orthogonal to each other. In the last part of this thesis, we deal with the very difficult but important topic of improving the generalisation capabilities of speech emotion recognition (SER) systems over different speakers and recording environments. This topic has been generally overlooked in the current research in this area. First we try the traditional methods used in automatic speech recognition (ASR) systems for improving the generalisation of SER in intra– and inter–database emotion classification. These traditional methods do improve the average accuracy of the emotion classifier. In this thesis, we identify these differences in the training and test data, due to speakers and acoustic environments, as a covariate shift. This shift is minimised by using importance weighting algorithms from the emerging field of transfer learning to guide the learning algorithm towards that training data which gives better representation of testing data. Our results show that importance weighting algorithms can be used to minimise the differences between the training and testing data. We also test the effectiveness of importance weighting algorithms on inter–database and cross-lingual emotion recognition. From these results, we draw conclusions about the universal nature of emotions across different languages.
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Yagci, Tayfun. "Target Classification And Recognition Using Underwater Acoustic Signals." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/3/12606373/index.pdf.
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Nowadays, fulfillment of the tactical operations in secrecy has great importance for especially subsurface and surface warfare platforms as a result of improvements in weapon technologies. Spreading out of the tactical operations to the larger areas has made discrimination of targets unavoidable. Due to enlargement of the weapon ranges and increasing subtle hostile threats as a result of improving technology, &ldquovisual&rdquo
target detection methods left the stage to the computerized acoustic signature detection and evaluation methods. Despite this, the research projects have not sufficiently addressed in the field of acoustic signature evaluation. This thesis work mainly investigates classification and recognition techniques with TRN / LOFAR signals, which are emitted from surface and subsurface platforms and proposes possible adaptations of existing methods that may give better results if they are used with these signals. Also a detailed comparison has been made about the experimental results with underwater acoustic signals.
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Dunn, Shane C. "Acoustic classification of benthic habitats in Tampa Bay." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002297.
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Philips, Scott M. "Perceptually-driven signal analysis for acoustic event classification /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5934.
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Wichert, Terry S., and Daniel Joseph Collins. "Feature based neural network acoustic transient signal classification." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/24169.
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Bissinger, Brett Bose N. K. Culver R. Lee. "Minimum hellinger distance classification of underwater acoustic signals." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4677/index.html.
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Roberts, Paul L. D. "Multi-view, broadband, acoustic classification of marine animals." Diss., [La Jolla] : University of California, San Diego, 2009. http://nsgl.gso.uri.edu/casg/casgy09003.pdf.
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Thesis (Ph. D.)--University of California, San Diego, 2009.Title from first page of PDF file (viewed June 16, 2009). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 141-155).
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Hammond, Tim R. "Classification of fish schools from acoustic survey data /." Thesis, Connect to this title online; UW restricted, 2000. http://hdl.handle.net/1773/5351.
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Sampan, Somkiat. "Neural Fuzzy Techniques in Vehicle Acoustic Signal Classification." Diss., Virginia Tech, 1998. http://hdl.handle.net/10919/30612.
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Vehicle acoustic signals have long been considered as unwanted traffic noise. In this research acoustic signals generated by each vehicle will be used to detect its presence and classify its type. Circular arrays of microphones were designed and built to detect desired signals and suppress unwanted ones. Circular arrays with multiple rings have an interesting and important property that is constant sidelobe levels. A modified genetic algorithm that can work directly with real numbers is used in the circular array design. It offers more effective ways to solve numerical problems than a standard genetic algorithm.
In classifier design two main paradigms are considered: multilayer perceptrons and adaptive fuzzy logic systems. A multilayer perceptron is a network inspired by biological neural systems. Even though it is far from a biological system, it possesses the capability to solve many interesting problems in variety fields. Fuzzy logic systems, on the other hand, were inspired by human capabilities to deal with fuzzy terms. Its structures and operations are based on fuzzy set theory and its operations. Adaptive fuzzy logic systems are fuzzy logic systems equipped with training algorithms so that its rules can be extracted or modified from available numerical data similar to neural networks. Both fuzzy logic systems and multilayer perceptrons have been proved to be universal function approximators. Since there are approximations in almost every stage, both of these system types are good candidates for classification systems.
In classification problems unequal learning of each class is normally encountered. This unequal learning may come from different learning difficulties and/or unequal numbers of training data from each class. The classifier tends to classify better for a well-learned class while doing poorly for other classes. Classification costs that may be different from class to class can be used to train and test a classifier. An error backpropagation algorithm can be modified so that the classification costs along with unequal learning factors can be used to control classifier learning during its training phase.Ph. D.
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Lazaridès, Ariane. "Classification trees for acoustic models : variations on a theme." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0016/MQ37139.pdf.
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Hadden, Scott Duncan. "Remote geotechnical classification of seabed sediments using acoustic techniques." Thesis, University of St Andrews, 2002. http://hdl.handle.net/10023/13953.
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Although the sonar amplitude of return is undoubtedly determined by the acoustic-sediment interaction at the seabed, the raw amplitude of return is of little practical use to geotechnical engineers. By focusing upon the relationships between the strength of the acoustic scattering and the roughness of the surficial seabed sediments, this research aims to derive a remote acoustic methodology that can be used to predict the geotechnical characteristics of the seabed sediment. The main field survey areas selected were Loch Earn, Scotland, and the Portsmouth coastal waters in the Solent, England, with the precise location of the field sites being determined by the distribution of differing sediment types. The sonar data was acquired by a 234kHz Interferometric Seabed Inspection Sonar system, which provided not only high precision and high resolution, but also extensive and very dense data coverage. These sonar datasets were then complemented by a sediment ground-truthing programme within the same area. Using trigonometry and the 'sonar equation' parameters, the complex post-processing of the bathymetric and acoustic data resulted in the generation of an acoustic roughness measurement. The sediment grain size analysis then followed standard techniques to derive values for the statistical roughness parameters of the sediment. The correlation between the acoustic and sediment roughness uncovered a good correlation between the mean grain size and also the finest modal value, with an increase in acoustic scattering strength reflecting an increase in the mean and finest modal grain sizes. The reversal of this correlation therefore enables a prediction of mean or finest modal grain size, thereby demonstrating an approach towards an 'unsupervised' acoustic sediment classification scheme. This study was carried out over a very narrow grain size range, from muds to fine sands, and therefore the addition of sonar datasets recorded over coarser sediments are required to complete the classification scheme.
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Edwards, Joseph Richard 1971. "Acoustic classification of buried objects with mobile sonar platforms." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37568.
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Thesis (Ph. D. in Ocean Engineering)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.Includes bibliographical references (p. 229-237).
In this thesis, the use of highly mobile sonar platforms is investigated for the purpose of acoustically classifying compact objects on or below the seabed. The extension of existing strategies, including synthetic aperture sonar and conventional imaging, are explored within the context of the buried object problem. In particular, the need to employ low frequencies for seabed penetration is shown to have a significant impact both due to the relative length of the characteristic scattering mechanisms and due to the interface effects on the target scattering. New sonar strategies are also shown that exploit incoherent wide apertures that are created by multiple sonar platforms. For example, target shape can be inverted by mapping the scattered field from the target with a team of receiver vehicles. A single sonar-adaptive sonar platform is shown to have the ability to perform hunting and classification tasks more efficiently than its pre-programmed counterpart. While the monostatic sonar platform is often dominated by the source component, the bistatic or passive receiver platform behavior is controlled by the target response. The sonar-adaptive platform trajectory, however, can result in the platform finishing its classification effort out of position to complete further tasks.
(cont.) Within the context of a larger mission, the use of predetermined adaptive behaviors is shown to provide improved detection and classification performance while minimizing the risk to the overall mission. Finally, it is shown that multiple sonar-adaptive platforms can be used to create new sonar strategies for hunting and classifying objects by shape and content. The ability to sample the scattered field from the target across a wide variety of positions allows an analysis of the aspect-dependent behavior of the target. The aspect-dependence of the specular returns indicate the shape of the target, while the secondary returns from an elastic target are also strongly aspect-dependent. These features are exploited for improved classification performance in the buried object hunting mission.
by Joseph R. Edwards.
Ph.D.in Ocean Engineering
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Vemula, Hari Charan. "Multiple Drone Detection and Acoustic Scene Classification with Deep Learning." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547384408540764.
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Evans, Naoko. "Automated vehicle detection and classification using acoustic and seismic signals." Thesis, University of York, 2010. http://etheses.whiterose.ac.uk/1151/.
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Security threats to important infrastructure cause problems to not only those who live nearby but also in a much wider sense. It is therefore desirable to consider the use of automated systems capable of detection and identification of potential threats. This thesis describes an investigation into acoustic and seismic methods for achieving such a system specifically for commercial road vehicles. Accurate algorithms have been developed for recognition of moving vehicles using fusion of acoustic and seismic signals. It has been found that seismic signals are less susceptible to interfering signals, making them optimal for detection of vehicles. Their much narrower bandwidth also increases processing efficiency and speed. Thus, the algorithm developed utilises firstly only seismic signals to detect vehicle presence, and then employs both acoustic and seismic signals for classifying type of the vehicle. The detection algorithm is purely time domain and uses seismic Log Energy together with a modification of Time Domain Signal Coding. The best detection accuracy obtained was 97.71 % with Support Vector Machine and 99.02 % with Learning Vector Quantisation Neural Networks. The classification algorithm to distinguish between trucks and cars utilises three relatively simple time domain methods: Zero-Crossing Rate, Log Energy and Autocorrelation of seismic signals; combined with LPC coefficients collected from acoustic signals. Classification with either SVM or LVQ reached 93.30 % or 80.80 % respectively. This study therefore has demonstrated it is possible to detect an approaching vehicle and classify its type by using acoustic and seismic signal processing.
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Brecht, B. M., A. Raabe, and A. Ziemann. "Acoustic anemometry and thermometry." Universität Leipzig, 2010. https://ul.qucosa.de/id/qucosa%3A16371.
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Acoustic travel-time measurement is a method for remote sensing of the atmosphere. The temperature-dependent sound speed as well as the flow field can be detected by measuring the travel time of a defined acoustic signal between a sound source and a receiver when the distance between them is known. In this study the properties of the flow field are reconstructed using reciprocal sound rays to separate the directionindependent sound speed from the effective sound velocity including the flow velocity component in direction of the sound path. The measurements are taken on a horizontal scale of about 2 m x 2 m. By measurements in interiors, where no flow of air exists, the temperature can be determined with an accuracy of 0.6°C and the flow component in direction of the sound path with an accuracy of 0.3 m/s. If flow of air exists the measurements gets complicated because the phase shifts, which have been detected by the receivers, cannot be corrected like it was possible without the influence of flow.
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TAKEDA, Kazuya, Norihide KITAOKA, and Makoto SAKAI. "Acoustic Feature Transformation Combining Average and Maximum Classification Error Minimization Criteria." Institute of Electronics, Information and Communication Engineers, 2010. http://hdl.handle.net/2237/14970.
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Bekiroglu, Yasemi. "Nonstationary feature extraction techniques for automatic classification of impact acoustic signals." Thesis, Högskolan Dalarna, Datateknik, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:du-3592.
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Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.
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Eshetu, Tefera Zegeye. "Impact Acoustic Testing for Classification of CGI Mechanical and Material properties." Thesis, KTH, Industriell produktion, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140320.
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Automotive industries have been putting extensive effort into producing engine materials considering resistance and weight of the engine material. This material should withstand higher combustion pressure and in the meantime should be lighter. Compacted Graphite Iron (CGI) is a material that could allow achieving these design requirements. But the variation of the CGI material and mechanical properties are very high within the given specifications. The thesis is focused on classifying CGI according to its material and mechanical properties. Impact Acoustic Testing method is a Non Destructive Testing method which is fast and might be able to classify the CGI materials based on its properties. The method can measure the structural response of a part. Its volumetric approach tests the whole part providing objective and quantitative results. The result was that the method could able to distinguish between gray iron and CGI, and could distinguish partly among CGI. Keywords: Non Destructive Testing, Impact Acoustic Testing, Compacted Graphite Iron
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Brink, Stefan. "Development of an acoustic classification system for predicting rock structural stability." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96985.
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Thesis (MSc)--Stellenbosch University, 2015.ENGLISH ABSTRACT: Rock falls are the cause of the majority of mining-related injuries and fatalities in deep tabular South African mines. The standard process of entry examination is performed before working shifts and after blasting to detect structurally loose rocks. This process is performed by a miner using a pinch bar to ‘sound’ a rock by striking it and making a judgement based on the frequency response of the resultant sound. The Electronic Sounding Device (ESD) developed by the CSIR aims to assist in this process by performing a concurrent prediction of the structural state of the rock based on the acoustic waveform generated in the sounding process. This project aimed to identify, develop and deploy an effective classification model to be used on the ESD to perform this assessment. The project was undertaken in three main stages: the collection of labelled acoustic samples from working areas; the extraction of descriptive features from the waveforms; and the competitive evaluation of suitable classification models. Acoustic samples of the sounding process were recorded at the Driefontein mine operation by teams of Gold Fields employees. The samples were recorded in working areas on each of the four reefs that were covered by the shafts of the mine complex. Samples were labelled as ‘safe’ or ‘unsafe’ to indicate an expert’s judgement of the rock’s structural state. A laboratory-controlled environment was also created to provide a platform from which to collect acoustic samples with objective labelling. Three sets of features were extracted from the acoustic waveforms to form a descriptive feature dataset: four statistical moments of the frequency distribution of the waveform formed; the average energy contained in 16 discrete frequency bands in the data; and 12 Mel Frequency Cepstral Coefficients (MFCCs). Classification models from four model families were competitively evaluated for best accuracy in predicting structural states. The models evaluated were k-nearest neighbours, self-organising maps, decision trees, random forests, logistic regression, neural networks, and support vector machines with radial basis function and polynomial kernels. The sensitivity of the models, i.e. their ability to avoid predicting a ‘safe’ status when the rock mass was actually loose, was used as the critical performance measure. A single-hidden-layer feed-forward neural network with 15 nodes in the hidden layer and a sigmoid activation function was found to best suited for acoustic classification on the ESD. Additional feature selection was performed to identify the optimised form of the model. The final model was successfully implemented on the ESD platform.
AFRIKAANSE OPSOMMING: Rotsstortings is die oorsaak van die meerderheid van mynbouverwante ongelukke en ongevalle in diep tabulêre Suid-Afrikaanse myne. Die standaard proses van pretoegang ondersoeke om strukturele los rotse te erken, word uitgevoer voor enige werkskof en na skietwerk. Dit word gedoen deur ‘n myner wat ‘n breekyster teen die rots kap en ‘n oordeel vel op die frekwensie weergawe van die gevolglike klank. Die ‘Elektroniese Klinking Toestel’ (Electronic Sounding Device, ESD) is ontwikkel deur die WNNR met die doel om die proses te ondersteun. Dit word gedoen deur ‘n gelyktydige voorspelling van die strukturele toestand gebaseer op die akoestiese golfvorm gegenereer in die proses van klinking. Die projek stel ten doel om ’n effektiewe klassifikasie-model te identifiseer, te ontwikkel en toe te pas in die ESD om hierdie assessering uit te voer. Die projek vind in drie stadiums plaas: die insameling van geëtiketteerde akoestiese monsters van die werkareas; die ekstraksie van beskrywende kenmerke van die golfvorms en die mededingende evaluering van geskikte klassifiseringsmodelle. Klinking akoestiese monsters is opgeneem by Driefontein mynbouoperasie deur spanne van Gold Fields se werknemers. Die akoestiese monsters is opgeneem in werkareas van elk van die vier goudriwwe wat deur die skagte van die mynkompleks gedek word. Monsters is as ‘veilig’ of ‘onveilig’ geëtiketteer as aanduiding van die ekspert se oordeel van die rots se strukturele toestand. ‘n Laboratorium gekontroleerde omgewing is ook geskep om ’n platform te skep vanwaar akoestiese monsters met objektiewe etikettering waargeneem word. Drie stelle van kenmerke is onttrek van die akoestiese golfvorms om ‘n beskrywende datastel van kenmerke te vorm: vier statistiese momente van die frekwensie verspreiding van die gevormde golfvorm; gemiddelde energie ingesluit in sestien diskrete frekwensiebande in die data; en twaalf ‘Mel Frequency Cepstrum Coefficients’ (MFCCs). Klassifikasie modelle van die vier modelsamestellings was kompeterend geëvalueer vir die beste akkuraatheid in voorspellings van strukturele toestande. Klassifikasie modelle het k-naaste bure, selforganiserende kaarte, besluitnemingsbome, lukrake woude, logistieke regressie, neurale netwerke en steun-vektor masjiene met radiale basisfunksie en polinominale kerne. Die meting van die sensitiwiteit van die modelle, met betrekking tot die vermoë van die modelle om veilige voorspellings te beperk wanneer die rotsmassa los is, was gebruik as ’n kritiese werksverrigtingsmeting. ‘n Enkel-verskuilde-laag neurale netwerk met 15 nodes in die verskuilde laag en ’n sigmoïde aktiveringsfunksie is gevind as die mees geskikte vir die ESD. Addisionele keuse van kenmerke is uitgevoer deur die geoptimiseerde vorm van die model te identifiseer. Die model was suksesvol geïmplementeer op die ESD platform.
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LeBien, John. "Automated Species Classification Methods for Passive Acoustic Monitoring of Beaked Whales." ScholarWorks@UNO, 2017. https://scholarworks.uno.edu/td/2417.
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The Littoral Acoustic Demonstration Center has collected passive acoustic monitoring data in the northern Gulf of Mexico since 2001. Recordings were made in 2007 near the Deepwater Horizon oil spill that provide a baseline for an extensive study of regional marine mammal populations in response to the disaster. Animal density estimates can be derived from detections of echolocation signals in the acoustic data. Beaked whales are of particular interest as they remain one of the least understood groups of marine mammals, and relatively few abundance estimates exist. Efficient methods for classifying detected echolocation transients are essential for mining long-term passive acoustic data. In this study, three data clustering routines using k-means, self-organizing maps, and spectral clustering were tested with various features of detected echolocation transients. Several methods effectively isolated the echolocation signals of regional beaked whales at the species level. Feedforward neural network classifiers were also evaluated, and performed with high accuracy under various noise conditions. The waveform fractal dimension was tested as a feature for marine biosonar classification and improved the accuracy of the classifiers. [This research was made possible by a grant from The Gulf of Mexico Research Initiative. Data are publicly available through the Gulf of Mexico Research Initiative Information & Data Cooperative (GRIIDC) at https://data.gulfresearchinitiative.org.] [DOIs: 10.7266/N7W094CG, 10.7266/N7QF8R9K]
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Hjelmervik, Karl Thomas. "Sonar false alarm rate suppression using classification methods based on acoustic modelling." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for elektronikk og telekommunikasjon, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-14912.
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The use of high–resolution, active sonar systems in littoral environments often results in high false alarm rates. False alarm rate inflation (FARI) and non–Rayleigh reverberation (NRR) are two well–documented causes. FARI may occur when the reverberation in the normaliser window is non–stationary, while NRR may occur when the sonar footprint is too small for the central limit theorem to apply for the scatterer statistics. The main originator for false alarms in littoral environments are either the sea floor itself or objects located on the sea floor. Automatic classification methods may be used to reduce the false alarm rate. Conventionally, advanced sonar processing or image processing techniques have been used directly on received data. Increased availability of environmental information allows for more sophisticated algorithms that employ acoustic modelling to extract more information from recorded data. This thesis addresses two topics of interest. The first topic is on how acoustic modelling combined with environmental knowledge may be used to increase the ability of anti–submarine warfare sonars to classify a detected target. The second topic is on how environmental uncertainty may be reduced in order to improve the fidelity of the proposed classification algorithms
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Mennitt, Daniel James. "Multiarray Passive Acoustic Localization and Tracking." Diss., Virginia Tech, 2008. http://hdl.handle.net/10919/29583.
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Wireless sensor networks and data fusion has received increasing attention in recent years, due to the ever increasing computational power, battery and wireless technology, and proliferation of sensor modalities. Notably, the application of acoustic sensors and arrays of sensors has expanded to encompass surveillance, teleconferencing, and sound source localization in adverse environments. The ability to passively locate and track acoustic sources, be they gunfire, animals, or geological events, is crucial to a wide range of applications. The challenge addressed herein is how to best utilize the massive amount of data collected from spatially distributed sensors. Localization in two acoustic propagation scenarios is addressed: the free-field assumption and the general case. In both cases, it is found that performance is highly dependent on the array-source geometry which in turn drives the design of localization strategies.
First, the general surveillance problem including signal detection, classification, data association, localization and tracking is studied. Signal detectors are designed with a focus on robustness and capacity for real time implementation. Specifics of the data association problem relevant to acoustic measurements are addressed. Assuming free-field propagation, a localization algorithm is developed to harness some of the vast potential and robust nature of a sensor networks. In addition, a prototypical sensor network has been constructed to accompany the theoretical development, address real world situations, and demonstrate applicability. Experimental results obtained confirm the practicality of theoretical models, support numerical results, and illustrate the effectiveness of the proposed strategies and the system as a whole.
In many situations of interest, obstacles to wave propagation such as terrain or buildings exist that provide unique challenges to localization. These obstacles introduce multiple paths, diffraction, and scattering into the propagation. The second part of this dissertation investigates localization in the general propagation scenario of a multi-wave, semi-reverberant environment characteristic of urban areas. Matched field processing is introduced as a feasible method and found to offer superior performance and flexibility over time reversal techniques. The effects of uncertainty in model parameters are studied in an urban setting. Multiarray processing methods are developed and strategies to mitigate the effects of model mismatch are established.Ph. D.
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Wood, JD, B. McCowan, R. Langbauer, J. Viljoen, and L. Hart. "Classification of African elephant Loxodonta Africana rumbles using acoustic parameters and cluster analysis." Bioacoustics, The International Journal of Animal Sound and its Recording, 2005. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001005.
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It has been suggested that African savanna elephants Loxodonta africana produce 31
different call types (Langbauer 2000). Various researchers have described these calls
by associating them with specific behavioural contexts. More recently Leong et al.
(2003) have attempted to classify elephant call types based on their physical
properties. They classified 8 acoustically distinct call types from a population of
captive elephants. This study focuses on one of these call types, the rumble, in a wild
population of elephants in Kruger National Park, South Africa. A single family group
of elephants was followed to record group behaviours and vocalizations from January
through August 2001. By measuring the physical properties of 663 rumbles and
subjecting these to cluster analysis, we present evidence that shows that rumbles can
be categorized by their physical properties and that the resulting rumble types are
associated with specific group behaviours. We characterize three types of rumbles that
differ significantly by ten acoustic parameters. Two rumble types were associated with
the elephant group feeding and resting, while the third was associated with socializing
and agitation.
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Selander, Christopher. "Development of embedded devices for automated acoustic resonance analysis in material quality classification." Thesis, Uppsala universitet, Signaler och System, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-234401.
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In this report we investigate whether an embedded system can be used to determine materials quality by analysing the acoustic resonance frequencies. Through experiments the necessary specifications are established and suitable circuits constructed. Signal analysis is performed by a FFT-based algorithm. We have verified that the system is succesful in detecting the five strongest resonance frequencies and list these in order of amplitude. By using embedded devices, it's possible to lower the cost of purchase as well as power consumption dramatically compared to alternative solutions.I denna rapport undersöker vi hurvida ett inbyggt system kan användas för att bestämma materialkvalité genom analys av akustiska resonansfrekvenser. Genom experiment fastställs de specifikationer systemet måste ha, varefter lämpliga kretsar konstrueras. Med hjälp av en FFT-baserad algoritm utförs viss signalanalys. Vi har verifierat att systemet framgångsrikt kan detektera de fem starkaste resonansfrekvenserna och lista dessa efter amplitud. Genom användandet av inbyggda system kan energiförbrukningen och inköpskostnaden bli mycket lägre än alternativa lösningar.
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Barbu, Madalina. "Acoustic Seabed and Target Classification using Fractional Fourier Transform and Time-Frequency Transform Techniques." ScholarWorks@UNO, 2006. http://scholarworks.uno.edu/td/480.
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An approach for processing sonar signals with the ultimate goal of ocean bottom sediment classification and underwater buried target classification is presented in this dissertation. Work reported for sediment classification is based on sonar data collected by one of the AN/AQS-20's sonars. Synthetic data, simulating data acquired by parametric sonar, is employed for target classification. The technique is based on the Fractional Fourier Transform (FrFT), which is better suited for sonar applications because FrFT uses linear chirps as basis functions. In the first stage of the algorithm, FrFT requires finding the optimum order of the transform that can be estimated based on the properties of the transmitted signal. Then, the magnitude of the Fractional Fourier transform for optimal order applied to the backscattered signal is computed in order to approximate the magnitude of the bottom impulse response. Joint time-frequency representations of the signal offer the possibility to determine the time-frequency configuration of the signal as its characteristic features for classification purposes. The classification is based on singular value decomposition of the time-frequency distributions applied to the impulse response. A set of the largest singular values provides the discriminant features in a reduced dimensional space. Various discriminant functions are employed and the performance of the classifiers is evaluated. A study of various classifiers' performance is carried-out for the proposed algorithm under two scenarios for determining the impulse response: FrFT method versus standard deconvolution method. Of particular interest for underwater under-sediment classification applications are long targets such as cables of various diameters, which need to be identified as different from other strong reflectors or point targets. Synthetic test data are used to exemplify and evaluate the proposed technique for target classification. The synthetic data simulates the impulse response of cylindrical targets buried in the seafloor sediments. Results are presented that illustrate the processing procedure. An important characteristic of this method is that good classification accuracy of an unknown target is achieved having only the response of a known target in the free field. The algorithm shows an accurate way to classify buried objects under various scenarios, with high probability of correct classification.
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Butt, Abdul Haleem. "Speech Assessment for the Classification of Hypokinetic Dysthria in Parkinson Disease." Thesis, Högskolan Dalarna, Datateknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:du-10041.
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The aim of this thesis is to investigate computerized voice assessment methods to classify between the normal and Dysarthric speech signals. In this proposed system, computerized assessment methods equipped with signal processing and artificial intelligence techniques have been introduced. The sentences used for the measurement of inter-stress intervals (ISI) were read by each subject. These sentences were computed for comparisons between normal and impaired voice. Band pass filter has been used for the preprocessing of speech samples. Speech segmentation is performed using signal energy and spectral centroid to separate voiced and unvoiced areas in speech signal. Acoustic features are extracted from the LPC model and speech segments from each audio signal to find the anomalies. The speech features which have been assessed for classification are Energy Entropy, Zero crossing rate (ZCR), Spectral-Centroid, Mean Fundamental-Frequency (Meanf0), Jitter (RAP), Jitter (PPQ), and Shimmer (APQ). Naïve Bayes (NB) has been used for speech classification. For speech test-1 and test-2, 72% and 80% accuracies of classification between healthy and impaired speech samples have been achieved respectively using the NB. For speech test-3, 64% correct classification is achieved using the NB. The results direct the possibility of speech impairment classification in PD patients based on the clinical rating scale.
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Tiago, Marcelo Moreira [UNESP]. "Classificação de sinais acústicos utilizando a transformada wavelet discreta e a decomposição de modo empírico: aplicações na área de alimentos." Universidade Estadual Paulista (UNESP), 2011. http://hdl.handle.net/11449/87064.
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Um dos setores de grande importância na indústria frigorífica é o responsável pelo esquarte- jamento de aves, no qual peças inteiras são separadas em partes menores para comercialização. O processo de esquartejamento pode ser feito de forma automática, através de máquinas de corte, ou por trabalhadores, que cortam as aves utilizando uma serra circular. Por ser um tra- balho manual e envolver uma lâmina de corte, a periculosidade desse tipo de trabalho é alta, de maneira que mesmo com o uso de uma luva de aço inox como equipamento de proteção, costumam ocorrer acidentes que podem variar desde pequenos cortes até amputação de parte da mão do trabalhador atingido. Neste trabalho, é apresentado um método de análise de sinais para evitar que esse tipo de acidente ocorra. Esse sistema baseia-se na análise dos sinais acústicos envolvidos gerados durante esse processo e são utilizados para desligar o motor que impulsiona a serra e acionar um sistema de frenagem em casos quando houver a ocorrência de acidentes. O problema é abordado utilizando inicialmente um filtro digital e, posteriormente, com as técni- cas de análise multirresolução apresentadas pelas wavelets. Além disso, empregou-se também a decomposição de modo empírico, que também realiza uma análise multirresolução dos sinais decompondo os mesmos em funções de modo intrínseco. Visando detectar o maior número possível de toques suaves de luva na serra sem que cortes de ossos de frango fossem confundi- dos com toques de luva, o sistema apresentou um índice de acertos de aproximadamente 70%, havendo a ocorrência de apenas 2% de falsos positivos. Além desse problema, abordou-se o caso de detecção de trinca em ovos, no qual o objetivo era separar ovos trincados de ovos in- teiros utilizando um sistema barato e eficiente...
One of the most important sectors in the meatpacking industry is chicken quartering, where whole pieces are cut into smaller ones. The quartering process can be done by automatic ma- chines or by manually cutting the chickens using a circular saw. The manual technique imposes physical risks for the workers, which wear protective stainless steel gloves. Small injuries or, in the worst case, amputation of part of the hand can occur in the event of an accident. In this work, we propose a methodology to prevent this type of accident, which is based on the anal- ysis of the acoustic signals generated during this process. In the event of an accident, the saw touches the metal glove, the acoustic signals are processed and used to turn off the engine that drives the saw and trigger a braking system. The problem is firstly analyzed using a digital filter and then with multiresolution techniques by wavelet analysis. In addition, the empirical mode decomposition technique is also employed, which also performs multiresolution analysis of sig- nals. These three techniques are implemented and compared. The method presented a 70% of successful detection of light touches of saw/glove and 2% of false positives, when a normal cut operation is detected as a saw/glove touch, in general occurring when cutting specific parts of bone. Besides this problem, the case of eggshell crack detection is studied, where the goal was to separate cracked eggs from intact eggs using an inexpensive and efficient system. A solenoid was used as a source of mechanical excitation and the resulting acoustic signals were acquired and processed. The same signal processing techniques were employed and compared, with small changes in parameters. As a result, it was possible to detect 80% of cracked eggs and 100% of intact eggs. The multiresolution technique... (Complete abstract click electronic access below)
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Ziemann, Astrid, Klaus Arnold, and Armin Raabe. "Acoustic tomography in the atmospheric surface layer." Universität Leipzig, 1998. https://ul.qucosa.de/id/qucosa%3A15081.
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Die vorgestellte Methode der akustischen Tomographie (Simultane Iterative Rekonstruktionstechnik) und ein spezieller Auswertungsalgorithmus können flächengemittelte Werte meteorologischer Größen direkt bereitstellen. Somit werden zur Validierung numerischer mikroskaliger Atmosphärenmodelle weitgehend konsistente Daten geliefert. Das Verfahren verwendet die horizontale Ausbreitung von Schallstrahlen in der atmosphärischen Bodenschicht. Um einen allgemeinen Überblick zur Schallausbreitung unter verschiedenen atmosphärischen Bedingungen zu erhalten, wird ein zweidimensionales Schallausbreitungsmodell
genutzt. Von Messungen der akustischen Laufzeit zwischen Sendern und Empfängern an verschiedenen Punkten in einem Meßfeld kann der Zustand der durchquerten Atmosphäre abgeschätzt werden. Die Ableitung flächengemittelter Werte für die Schallgeschwindigkeit und der daraus deduzierten Lufttemperatur resultiert aus der Inversion der Laufzeitwerte für alle möglichen Schallwege. Das angewandte zweidimensionale Tomographiemodell mit geradliniger Schallstrahlapproximation stellt dabei geringe Computeranforderungen und ist auch während des online-Betriebes einfach zu handhaben.The presented method of acoustic tomography (Simultaneous Iterative Reconstruction Technique) and a special algorithm of analysis can directly provide area averaged values of meteorological quantities. As a result rather consistent data will be delivered for validation of numerical atmospheric rnicro-scale models. The procedure uses the horizontal propagation of sound waves in the atmospheric surface layer. To obtain a general overview of the sound propagation under various atmospheric conditions a two-dimensional ray-tracing model is used. The state of the crossed atmosphere can be estimated from measurements of acoustic travel time between sources and receivers on different points in an tomographic array. Derivation of area averaged values of the sound speed and furthermore of air temperature results from the inversion of travel time values for all possible acoustic paths. Thereby, the applied straight-ray two-dimensional tomographic model is characterised as a method with small computational requirements and simple handling, especially, during online working.
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Nouri, Arash. "Correlation-Based Detection and Classification of Rail Wheel Defects using Air-coupled Ultrasonic Acoustic Emissions." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78139.
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Defected wheel are one the major reasons endangered state of railroad vehicles safety statue, due to vehicle derailment and worsen the quality of freight and passenger transportation. Therefore, timely defect detection for monitoring and detecting the state of defects is highly critical.
This thesis presents a passive non-contact acoustic structural health monitoring approach using ultrasonic acoustic emissions (UAE) to detect certain defects on different structures, as well as, classifying the type of the defect on them. The acoustic emission signals used in this study are in the ultrasonic range (18-120 kHz), which is significantly higher than the majority of the research in this area thus far. For the proposed method, an impulse excitation, such as a hammer strike, is applied to the structure. In addition, ultrasound techniques have higher sensitivity to both surface and subsurface defects, which make the defect detection more accurate. Three structures considered for this study are: 1) a longitudinal beam, 2) a lifting weight, 3) an actual rail-wheel. A longitudinal beam was used at the first step for a better understanding of physics of the ultrasound propagation from the defect, as well, develop a method for extracting the signature response of the defect. Besides, the inherent directionality of the ultrasound microphone increases the signal to noise ratio (SNR) and could be useful in the noisy areas. Next, by considering the ultimate goal of the project, lifting weight was chosen, due to its similarity to the ultimate goal of this project that is a rail-wheel. A detection method and metric were developed by using the lifting weight and two type of synthetic defects were classified on this structure. Also, by using same extracted features, the same types of defects were detected and classified on an actual rail-wheel.Master of Science
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Ferreira, Anthony Paul Couto. "Acústica de edifícios: estudo de impacto económico associado ao método de classificação acústica." Master's thesis, Universidade de Évora, 2016. http://hdl.handle.net/10174/18205.
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O conforto acústico nas habitações é necessário ao bem-estar e saúde dos usufrutuários. Este conforto é obtido principalmente pela ausência e isolamento de ruídos, permitindo assim um repouso tranquilo e regenerador. Como tal o conforto acústico nas habitações é um campo de estudo muito importante na engenharia civil.
Uma análise global deste conforto acústico ocorre quando se consideram os fatores internos e externos à habitação. Ou seja, quando a acústica da vizinhança, do edifício e da habitação (fração) são analisadas individualmente e em conjunto.
Em Portugal a análise da acústica das habitações e da sua envolvente está regulamentada pelo Regulamento dos Requisitos Acústicos dos Edifícios (RRAE) e pelo Regulamento Geral do Ruído (RGR). Nestes regulamentos são analisados os comportamentos acústicos de cada componente individual, como tal, a análise global do comportamento e conforto acústico não existe.
De modo a eliminar esta ausência, em 2013, o Laboratório Nacional de Engenharia Civil (LNEC) introduziu um método global de análise, o Método LNEC para avaliação e classificação da qualidade acústica de edifícios habitacionais. Este “Método LNEC” efetua a análise global pelo estudo da acústica na “Vizinhança”, no “Edifício” e na “Habitação”. Nesta análise são pontuados os resultados de vários indicadores físicos e analíticos de cada componente (elementos). Estes valores são depois integrados num único valor, o Nível de Avaliação Acústica (NAA), ao qual corresponde uma Classe Acústica LNEC que permite facilmente identificar a qualidade acústica global sentida.
Esta Classe Acústica LNEC permite também comparar o conforto acústico global entre vários fogos. Contudo não existe ainda uma estimativa relativa ao custo de reabilitação necessário para ascender entre Classes Acústicas LNEC.
Nesta dissertação é feita a determinação deste custo de reabilitação necessário para ascender entre classes, criando assim uma ferramenta de análise do impacto económico do Método LNEC; BUILDING ACOUSTICS: ECONOMIC IMPACT ASSOCIATED TO THE ACOUSTIC CLASSIFICATION METHOD
ABSTRACT:
The acoustic comfort in dwellings is necessary for the wellbeing and health of the users. This comfort is achieved mainly by the absence of noise and noise insulation, thus enabling a quiet and regenerating rest. As such the acoustic comfort in homes is a very important field in civil engineering.
A global analysis of this acoustic comfort occurs when the external and internal factors are considered in the analysis. It happens when the acoustics of the vicinity, the building and the lodging are analyzed individually and together.
In Portugal the acoustic analysis of houses and its surroundings is regulated by the Regulations of Acoustic Requirements for Buildings (Regulamento dos Requisitos Acústicos dos Edifícios RRAE) and by the General Regulations of Noise (Regulamento Geral do Ruído RGR). This two regulatory laws only limit the acoustics of each individual component, resulting that the global analysis of the acoustics comfort sensed in dwellings does not occur.
To eliminate this problem, in 2013, the National Laboratory of Civil Engineering (Laboratório Nacional de Engenharia Civil LNEC) introduced a comprehensive method of analysis that fulfill this void doing a global analysis. This method, the Método LNEC para avaliação e classificação da qualidade acústica de edifícios habitacionais makes the overall analysis stuying the acoustics in the “Vicinity”, in the “Building” and in the “Dwelling”. It analyses each acoustic value and scors each one of them in order to be integrated in a global value. To this global value (NAA) corresponds an LNEC Acoustic Classe (Classe Acústica LNEC). This global analysis of acoustics (envelope system) allows an easy and correct way to synthetize the acoustic comfort, also the Classe Acústica LNEC allows comparisons between different houses.
Nevertheless there isn’t still a mechanism to estimate the rehabilitation cost of ascending between LNEC Acoustic Classes. On this dissertation, it is determined this important tool to evaluate the economic impact of acoustic rehabilitations.
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Tiago, Marcelo Moreira. "Classificação de sinais acústicos utilizando a transformada wavelet discreta e a decomposição de modo empírico : aplicações na área de alimentos /." Ilha Solteira: [s.n.], 2011. http://hdl.handle.net/11449/87064.
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Orientador: Ricardo Tokio HigutiBanca: Francisco Villarreal Alvarado
Banca: Washington Luiz de Barros Melo
Resumo: Um dos setores de grande importância na indústria frigorífica é o responsável pelo esquarte- jamento de aves, no qual peças inteiras são separadas em partes menores para comercialização. O processo de esquartejamento pode ser feito de forma automática, através de máquinas de corte, ou por trabalhadores, que cortam as aves utilizando uma serra circular. Por ser um tra- balho manual e envolver uma lâmina de corte, a periculosidade desse tipo de trabalho é alta, de maneira que mesmo com o uso de uma luva de aço inox como equipamento de proteção, costumam ocorrer acidentes que podem variar desde pequenos cortes até amputação de parte da mão do trabalhador atingido. Neste trabalho, é apresentado um método de análise de sinais para evitar que esse tipo de acidente ocorra. Esse sistema baseia-se na análise dos sinais acústicos envolvidos gerados durante esse processo e são utilizados para desligar o motor que impulsiona a serra e acionar um sistema de frenagem em casos quando houver a ocorrência de acidentes. O problema é abordado utilizando inicialmente um filtro digital e, posteriormente, com as técni- cas de análise multirresolução apresentadas pelas wavelets. Além disso, empregou-se também a decomposição de modo empírico, que também realiza uma análise multirresolução dos sinais decompondo os mesmos em funções de modo intrínseco. Visando detectar o maior número possível de toques suaves de luva na serra sem que cortes de ossos de frango fossem confundi- dos com toques de luva, o sistema apresentou um índice de acertos de aproximadamente 70%, havendo a ocorrência de apenas 2% de falsos positivos. Além desse problema, abordou-se o caso de detecção de trinca em ovos, no qual o objetivo era separar ovos trincados de ovos in- teiros utilizando um sistema barato e eficiente... (Resumo completo, clicar acesso eletrônico abaixo)
Abstract: One of the most important sectors in the meatpacking industry is chicken quartering, where whole pieces are cut into smaller ones. The quartering process can be done by automatic ma- chines or by manually cutting the chickens using a circular saw. The manual technique imposes physical risks for the workers, which wear protective stainless steel gloves. Small injuries or, in the worst case, amputation of part of the hand can occur in the event of an accident. In this work, we propose a methodology to prevent this type of accident, which is based on the anal- ysis of the acoustic signals generated during this process. In the event of an accident, the saw touches the metal glove, the acoustic signals are processed and used to turn off the engine that drives the saw and trigger a braking system. The problem is firstly analyzed using a digital filter and then with multiresolution techniques by wavelet analysis. In addition, the empirical mode decomposition technique is also employed, which also performs multiresolution analysis of sig- nals. These three techniques are implemented and compared. The method presented a 70% of successful detection of light touches of saw/glove and 2% of false positives, when a normal cut operation is detected as a saw/glove touch, in general occurring when cutting specific parts of bone. Besides this problem, the case of eggshell crack detection is studied, where the goal was to separate cracked eggs from intact eggs using an inexpensive and efficient system. A solenoid was used as a source of mechanical excitation and the resulting acoustic signals were acquired and processed. The same signal processing techniques were employed and compared, with small changes in parameters. As a result, it was possible to detect 80% of cracked eggs and 100% of intact eggs. The multiresolution technique... (Complete abstract click electronic access below)
Mestre
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Tucker, Simon. "An ecological approach to the classification of transient underwater acoustic events : perceptual experiments and auditory models." Thesis, University of Sheffield, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.401126.
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Kirchner, William Thomas. "Ultrasonic acoustic health monitoring of ball bearings using neural network pattern classification of power spectral density." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/36130.
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This thesis presents a generic passive non-contact based acoustic health monitoring approach using ultrasonic acoustic emissions (UAE) to facilitate classification of bearing health via neural networks. This generic approach is applied to classifying the operating condition of conventional ball bearings. The acoustic emission signals used in this study are in the ultrasonic range (20-120 kHz), which is significantly higher than the majority of the research in this area thus far. A direct benefit of working in this frequency range is the inherent directionality of the microphones capable of measurement in this range, which becomes particularly useful when operating in environments with low signal-to-noise ratios. Using the UAE power spectrum signature, it is possible to pose the health monitoring problem as a multi-class classification problem, and make use of a multi-layer artificial neural network (ANN) to classify the UAE signature. One major problem limiting the usefulness of ANN's for failure classification is the need for large quantities of training data. Artificial training data, based on statistical properties of a significantly smaller experimental data set is created using the combination of a normal distribution and a coordinate transformation. The artificial training data provides a sufficient sized data set to train the neural network, as well as overcome the curse of dimensionality. The combination of the artificial training methods and ultrasonic frequency range being used results in an approach generic enough to suggest that this particular method is applicable to a variety of systems and components where persistent UAE exist.Master of Science
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Hemminger, Thomas Lee. "A real-time neural-net computing approach to the detection and classification of underwater acoustic transients." Case Western Reserve University School of Graduate Studies / OhioLINK, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=case1056044506.
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Arnold, Klaus, Astrid Ziemann, and Armin Raabe. "Acoustic Tomography inside a small surface layer." Universität Leipzig, 2002. https://ul.qucosa.de/id/qucosa%3A15219.
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Acoustic travel time tomography is presented as an experimental technique for remote monitoring of spatially averaged meteorological quantities, such as the virtual air temperature and the horizontal wind speed. This ground based remote sensing technique uses the nearly horizontal propagation of sound waves in the atmospheric surface layer. Here the acoustic travel time tomography was applied by measuring the travel time at defined propagation paths between several sound sources and receivers. The resulting sound speed was used to obtain estimates of the meteorological parameters. Several measuring campaigns were carried out to compare the acoustically derived data with conventional systems. The results of a cross validation during a field experiment in autumn 2000 are presented, where receivers at different heights above the ground were used.Die Akustische Laufzeittomographie wird als ein Verfahren zur Fernerkundung räumlich gemittelter Größen, wie der virtuellen Temperatur und der horizontalen Windgeschwindigkeit, vorgestellt. Dieses bodengebundene Fernerkundungsverfahren beruht auf der annährend horizontalen Schallausbreitung in der atmosphärischen Grenzschicht. Das hier angewendete Verfahren der Laufzeittomographie beruht auf der Bestimmung der Ausbreitungszeit von Schallwellen zwischen mehreren Schallsendern und -empfängern. Die daraus abgeleitete Schallgeschwindigkeit liefert eine Information über die interessierenden meteorologischen Parameter. Eine Reihe von Feldexperimenten wurde durchgeführt mit dem Ziel, die akustisch bestimmten Größen mit denen konventioneller Verfahren zu vergleichen. Hier werden die Ergebnisse eines Vergleiches im Herbst 2000 präsentiert, bei dem die Schallempfänger in unterschiedlichen Höhen über dem Boden angebracht wurden.
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Butko, Taras. "Feature selection for multimodal: acoustic event detection." Doctoral thesis, Universitat Politècnica de Catalunya, 2011. http://hdl.handle.net/10803/32176.
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The detection of the Acoustic Events (AEs) naturally produced in a meeting room may help to describe the human and social activity. The automatic description of interactions between humans and environment can be useful for providing: implicit assistance to the people inside the room, context-aware and content-aware information requiring a minimum of human attention or interruptions, support for high-level analysis of the underlying acoustic scene, etc. On the other hand, the recent fast growth of available audio or audiovisual content strongly demands tools for analyzing, indexing, searching and retrieving the available documents. Given an audio document, the first processing step usually is audio segmentation (AS), i.e. the partitioning of the input audio stream into acoustically homogeneous regions which are labelled according to a predefined broad set of classes like speech, music, noise, etc. Acoustic event detection (AED) is the objective of this thesis work. A variety of features coming not only from audio but also from the video modality is proposed to deal with that detection problem in meeting-room and broadcast news domains. Two basic detection approaches are investigated in this work: a joint segmentation and classification using Hidden Markov Models (HMMs) with Gaussian Mixture Densities (GMMs), and a detection-by-classification approach using discriminative Support Vector Machines (SVMs). For the first case, a fast one-pass-training feature selection algorithm is developed in this thesis to select, for each AE class, the subset of multimodal features that shows the best detection rate. AED in meeting-room environments aims at processing the signals collected by distant microphones and video cameras in order to obtain the temporal sequence of (possibly overlapped) AEs that have been produced in the room. When applied to interactive seminars with a certain degree of spontaneity, the detection of acoustic events from only the audio modality alone shows a large amount of errors, which is mostly due to the temporal overlaps of sounds. This thesis includes several novelties regarding the task of multimodal AED. Firstly, the use of video features. Since in the video modality the acoustic sources do not overlap (except for occlusions), the proposed features improve AED in such rather spontaneous scenario recordings. Secondly, the inclusion of acoustic localization features, which, in combination with the usual spectro-temporal audio features, yield a further improvement in recognition rate. Thirdly, the comparison of feature-level and decision-level fusion strategies for the combination of audio and video modalities. In the later case, the system output scores are combined using two statistical approaches: weighted arithmetical mean and fuzzy integral. On the other hand, due to the scarcity of annotated multimodal data, and, in particular, of data with temporal sound overlaps, a new multimodal database with a rich variety of meeting-room AEs has been recorded and manually annotated, and it has been made publicly available for research purposes.La detecció d'esdeveniments acústics (Acoustic Events -AEs-) que es produeixen naturalment en una sala de reunions pot ajudar a descriure l'activitat humana i social. La descripció automàtica de les interaccions entre els éssers humans i l'entorn pot ser útil per a proporcionar: ajuda implícita a la gent dins de la sala, informació sensible al context i al contingut sense requerir gaire atenció humana ni interrupcions, suport per a l'anàlisi d'alt nivell de l'escena acústica, etc. La detecció i la descripció d'activitat és una funcionalitat clau de les interfícies perceptives que treballen en entorns de comunicació humana com sales de reunions. D'altra banda, el recent creixement ràpid del contingut audiovisual disponible requereix l'existència d'eines per a l'anàlisi, indexació, cerca i recuperació dels documents existents. Donat un document d'àudio, el primer pas de processament acostuma a ser la seva segmentació (Audio Segmentation (AS)), és a dir, la partició de la seqüència d'entrada d'àudio en regions acústiques homogènies que s'etiqueten d'acord amb un conjunt predefinit de classes com parla, música, soroll, etc. De fet, l'AS pot ser vist com un cas particular de la detecció d’esdeveniments acústics, i així es fa en aquesta tesi. La detecció d’esdeveniments acústics (Acoustic Event Detection (AED)) és un dels objectius d'aquesta tesi. Es proposa tot una varietat de característiques que provenen no només de l'àudio, sinó també de la modalitat de vídeo, per fer front al problema de la detecció en dominis de sala de reunions i de difusió de notícies. En aquest treball s'investiguen dos enfocaments bàsics de detecció: 1) la realització conjunta de segmentació i classificació utilitzant models de Markov ocults (Hidden Markov Models (HMMs)) amb models de barreges de gaussianes (Gaussian Mixture Models (GMMs)), i 2) la detecció per classificació utilitzant màquines de vectors suport (Support Vector Machines (SVM)) discriminatives. Per al primer cas, en aquesta tesi es desenvolupa un algorisme de selecció de característiques ràpid d'un sol pas per tal de seleccionar, per a cada AE, el subconjunt de característiques multimodals que aconsegueix la millor taxa de detecció. L'AED en entorns de sales de reunió té com a objectiu processar els senyals recollits per micròfons distants i càmeres de vídeo per tal d'obtenir la seqüència temporal dels (possiblement superposats) esdeveniments acústics que s'han produït a la sala. Quan s'aplica als seminaris interactius amb un cert grau d'espontaneïtat, la detecció d'esdeveniments acústics a partir de només la modalitat d'àudio mostra una gran quantitat d'errors, que és sobretot a causa de la superposició temporal dels sons. Aquesta tesi inclou diverses contribucions pel que fa a la tasca d'AED multimodal. En primer lloc, l'ús de característiques de vídeo. Ja que en la modalitat de vídeo les fonts acústiques no se superposen (exceptuant les oclusions), les característiques proposades Resum iv milloren la detecció en els enregistraments en escenaris de caire espontani. En segon lloc, la inclusió de característiques de localització acústica, que, en combinació amb les característiques habituals d'àudio espectrotemporals, signifiquen nova millora en la taxa de reconeixement. En tercer lloc, la comparació d'estratègies de fusió a nivell de característiques i a nivell de decisions, per a la utilització combinada de les modalitats d'àudio i vídeo. En el darrer cas, les puntuacions de sortida del sistema es combinen fent ús de dos mètodes estadístics: la mitjana aritmètica ponderada i la integral difusa. D'altra banda, a causa de l'escassetat de dades multimodals anotades, i, en particular, de dades amb superposició temporal de sons, s'ha gravat i anotat manualment una nova base de dades multimodal amb una rica varietat d'AEs de sala de reunions, i s'ha posat a disposició pública per a finalitats d'investigació. Per a la segmentació d'àudio en el domini de difusió de notícies, es proposa una arquitectura jeràrquica de sistema, que agrupa apropiadament un conjunt de detectors, cada un dels quals correspon a una de les classes acústiques d'interès. S'han desenvolupat dos sistemes diferents de SA per a dues bases de dades de difusió de notícies: la primera correspon a gravacions d'àudio del programa de debat Àgora del canal de televisió català TV3, i el segon inclou diversos segments d'àudio del canal de televisió català 3/24 de difusió de notícies. La sortida del primer sistema es va utilitzar com a primera etapa dels sistemes de traducció automàtica i de subtitulat del projecte Tecnoparla, un projecte finançat pel govern de la Generalitat en el que es desenvoluparen diverses tecnologies de la parla per extreure tota la informació possible del senyal d'àudio. El segon sistema d'AS, que és un sistema de detecció jeràrquica basat en HMM-GMM amb selecció de característiques, ha obtingut resultats competitius en l'avaluació de segmentació d'àudio Albayzín2010. Per acabar, val la pena esmentar alguns resultats col·laterals d’aquesta tesi. L’autor ha sigut responsable de l'organització de l'avaluació de sistemes de segmentació d'àudio dins de la campanya Albayzín-2010 abans esmentada. S'han especificat les classes d’esdeveniments, les bases de dades, la mètrica i els protocols d'avaluació utilitzats, i s'ha realitzat una anàlisi posterior dels sistemes i els resultats presentats pels vuit grups de recerca participants, provinents d'universitats espanyoles i portugueses. A més a més, s'ha implementat en la sala multimodal de la UPC un sistema de detecció d'esdeveniments acústics per a dues fonts simultànies, basat en HMM-GMM, i funcionant en temps real, per finalitats de test i demostració.
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Yapanel, Umit. "Acoustic modeling and speaker normalization strategies with application to robust in-vehicle speech recognition and dialect classification." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/cr/colorado/fullcit?p3190395.
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Skarke, Adam D. "Application of chirp sonar acoustic reflection coefficient for sea floor sediment classification results from the Delaware Estuary /." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 109 p, 2008. http://proquest.umi.com/pqdweb?did=1459905621&sid=2&Fmt=2&clientId=8331&RQT=309&VName=PQD.
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Ziemann, Astrid, Klaus Arnold, and Armin Raabe. "Acoustic tomography as a method to characterize measuring sites." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 22 = Meteorologische Arbeiten aus Leipzig ; 6 (2001), S. 50-59, 2001. https://ul.qucosa.de/id/qucosa%3A15199.
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The method of acoustic tomography, based on external sonic energy, is applied inside the atmospheric surface layer to observe near-surface temperature fields. Important advantages of this technique as compared to other measurement methods are their remote-sensing capacity and the possibility to directly derivate area-averaged meteorological quantities. The needed input data for the tomographically inverse algorithm are provided by the interaction of sound waves with the scanned atmospheric layer. The resulting horizontal slices lead to statements on the inhomogeneity of the underlying surface which may result in noticeable difficulties during the analysis of measuring campaigns with conventional methods.Die auf der Aussendung von Schallenergie basierende Methode der akustischen Tomographie wird in der atmosphärischen Bodenschicht angewendet, um bodennahe Temperaturfelder zu beobachten. Bedeutende Vorteile dieses Verfahrens im Vergleich zu anderen Meßmethoden sind die Fernerkundungskapazität und die Möglichkeit, flächengemittelte Werte meteorologischer Größen direkt abzuleiten. Die für den tomographischen Invertierungsalgorithmus benötigten Eingangsdaten werden durch die Wechselwirkung von Schallwellen mit der durchstrahlten Luftschicht bereitgestellt. Die resultierenden horizontalen Schnittbilder führen zu Darstellungen der Inhomogenität der Oberfläche. Letztere können beachtliche Schwierigkeiten während der Analyse von Messkampagnen mit konventionellen Methoden hervorrufen.
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Nieuwoudt, Christoph. "Cross-language acoustic adaptation for automatic speech recognition." Thesis, Pretoria : [s.n.], 2000. http://upetd.up.ac.za/thesis/available/etd-01062005-071829.
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Späth, Bastian, Matthias Philipp, and Thomas Bartnitzki. "Machine performance and acoustic fingerprints of cutting and drilling." TU Bergakademie Freiberg, 2017. https://tubaf.qucosa.de/id/qucosa%3A23182.
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‘It is always dark ahead of the pick!’ This centuries-old miners’ expression still reveals the uncertainty about the upcoming rock properties during exploration and extraction processes. It is still tough to predict what a drill rig or a cutting machine will experience during operation. However, in terms of safety, energy consumption and the performance of the whole machine it would be beneficial to be able to monitor such an extraction process. Hence, different sensors or sensor combinations are tested during cutting and drilling processes within RealTime Mining project. First aim is to depict the machine performance of the machine at any time. In a second step sensor information is also used to conclude on mechanical rock properties during the process.
Measuring the machine performance for cutting and drilling is quite similar and has been condensed under the terms Monitoring-While-Cutting (MWC) respectively Monitoring-While-Drilling (MWD). Both monitoring systems contain a bundle of sensors to depict the whole process. As an example, the energy demand of such a machine can be determined by measuring the power consumption of the engines constantly. Furthermore, the process parameters like advance rates and drilling or cutting speed have to be evaluated as well to be able to depict the whole extraction machine.
To conclude on mechanical rock properties several other sensor solutions have been tested and finally integrated into those monitoring systems. One of the most important rock properties for drilling and cutting is the rock strength. Increasing rock strength during an extraction process leads to increasing forces that are needed to break a certain amount of rock. Hence, e.g. measuring the torque of a drill string or the cutting forces can be an indicator on rock resistance or rock strength. Not minor important, is the characteristic rock breakage behavior which can be classified by the use of ‘acoustic’ sensors. Dependent on the rock properties that currently is drilled or cut through a characteristic fracture occurs in front of the tool. This results in audible and also inaudible characteristic acoustic waves that propagate through the machine body and can be gathered on the machine by piezo-electric sensors. The interpretation of these signals could lead to a material classification already during the extraction process.
Several tests of these sensor technologies have been conducted in laboratory environment as well in field tests. The most promising results are going to be presented.
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Flocon-Cholet, Joachim. "Classification audio sous contrainte de faible latence." Thesis, Rennes 1, 2016. http://www.theses.fr/2016REN1S030/document.
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Cette thèse porte sur la classification audio sous contrainte de faible latence. La classification audio est un sujet qui a beaucoup mobilisé les chercheurs depuis plusieurs années. Cependant, on remarque qu’une grande majorité des systèmes de classification ne font pas état de contraintes temporelles : le signal peut être parcouru librement afin de rassembler les informations nécessaires pour la prise de décision (on parle alors d’une classification hors ligne). Or, on se place ici dans un contexte de classification audio pour des applications liées au domaine des télécommunications. Les conditions d’utilisation sont alors plus sévères : les algorithmes fonctionnent en temps réel et l’analyse du signal et le traitement associé se font à la volée, au fur et à mesure que le signal audio est transmis. De fait, l’étape de classification audio doit également répondre aux contraintes du temps réel, ce qui affecte son fonctionnement de plusieurs manières : l’horizon d’observation du signal se voit nécessairement réduit aux instants présents et à quelques éléments passés, et malgré cela, le système doit être fiable et réactif. Dès lors, la première question qui survient est : quelle stratégie de classification peut-on adopter afin de faire face aux exigences du temps réel ? On retrouve dans littérature deux grandes approches permettant de répondre à des contraintes temporelles plus ou moins fortes : la classification à la trame et la classification sur segment. Dans le cadre d’une classification à la trame, la décision est prise en se basant uniquement sur des informations issues de la trame audio courante. La classification sur segment, elle, exploite une information court-terme en utilisant les informations issues de la trame courante et de quelques trames précédentes. La fusion des données se fait via un processus d’intégration temporelle qui consiste à extraire une information pertinente basée sur l’évolution temporelle des descripteurs audio. À partir de là, on peut s’interroger pour savoir quelles sont les limites de ces stratégies de classification ? Une classification à la trame et une classification sur segment peuvent-elles être utilisées quel que soit le contexte ? Est-il possible d’obtenir des performances convenables avec ces deux approches ? Quelle mode de classification permet de produire le meilleur rapport entre performance de classification et réactivité ? Aussi, pour une classification sur segment, le processus d’intégration temporelle repose principalement sur des modélisation statistiques mais serait-il possible de proposer d’autres approches ? L’exploration de ce sujet se fera à travers plusieurs cas d’étude concrets. Tout d’abord, dans le cadre des projets de recherche à Orange Labs, nous avons pu contribuer au développement d’un nouvel algorithme de protection acoustique, visant à supprimer très rapidement des signaux potentiellement dangereux pour l’auditeur. La méthode mise au point, reposant sur la proposition de trois descripteurs audio, montre un taux de détection élevé tout en conservant un taux de fausse alarme très bas, et ce, quelles que soient les conditions d’utilisation. Par la suite, nous nous sommes intéressés plus en détail à l’utilisation de l’intégration temporelle des descripteurs dans un cadre de classification audio faible latence. Pour cela, nous avons proposé et évalué plusieurs méthodologies d’utilisation de l’intégration temporelle permettant d’obtenir le meilleur compromis entre performance globale et réactivité. Enfin, nous proposons une autre manière d’exploiter l’information temporelle des descripteurs. L’approche proposée s’appuie sur l’utilisation des représentations symboliques permettant de capter la structure temporelle des séries de descripteurs. L’idée étant ensuite de rechercher des motifs temporels caractéristiques des différentes classes audio. Les expériences réalisées montrent le potentiel de cette approcheThis thesis focuses on audio classification under low-latency constraints. Audio classification has been widely studied for the past few years, however, a large majority of the existing work presents classification systems that are not subject to temporal constraints : the audio signal can be scanned freely in order to gather the needed information to perform the decision (in that case, we may refer to an offline classification). Here, we consider audio classification in the telecommunication domain. The working conditions are now more severe : algorithms work in real time and the analysis and processing steps are now operated on the fly, as long as the signal is transmitted. Hence, the audio classification step has to meet the real time constraints, which can modify its behaviour in different ways : only the current and the past observations of the signal are available, and, despite this fact the classification system has to remain reliable and reactive. Thus, the first question that occurs is : what strategy for the classification can we adopt in order to tackle the real time constraints ? In the literature, we can find two main approaches : the frame-level classification and the segment-level classification. In the frame-level classification, the decision is performed using only the information extracted from the current audio frame. In the segment-level classification, we exploit a short-term information using data computed from the current and few past frames. The data fusion here is obtained using the process of temporal feature integration which consists of deriving relevant information based on the temporal evolution of the audio features. Based on that, there are several questions that need to be answered. What are the limits of these two classification framework ? Can an frame-level classification and a segment-level be used efficiently for any classification task ? Is it possible to obtain good performance with these approaches ? Which classification framework may lead to the best trade-off between accuracy and reactivity ? Furthermore, for the segment-level classification framework, the temporal feature integration process is mainly based on statistical models, but would it be possible to propose other methods ? Throughout this thesis, we investigate this subject by working on several concrete case studies. First, we contribute to the development of a novel audio algorithm dedicated to audio protection. The purpose of this algorithm is to detect and suppress very quickly potentially dangerous sounds for the listener. Our method, which relies on the proposition of three features, shows high detection rate and low false alarm rate in many use cases. Then, we focus on the temporal feature integration in a low-latency framework. To that end, we propose and evaluate several methodologies for the use temporal integration that lead to a good compromise between performance and reactivity. Finally, we propose a novel approach that exploits the temporal evolution of the features. This approach is based on the use of symbolic representation that can capture the temporal structure of the features. The idea is thus to find temporal patterns that are specific to each audio classes. The experiments performed with this approach show promising results
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Caillat, Marjolaine. "Assessing and correcting for the effects of species misclassification during passive acoustic surveys of cetaceans." Thesis, University of St Andrews, 2013. http://hdl.handle.net/10023/4209.
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In conservation ecology, abundance estimates are an important factor from which management decisions are based. Methods to estimate abundance of cetaceans from visual detections are largely developed, whereas parallel methods based on passive acoustic detections are still in their infancy. To estimate the abundance of cetacean species using acoustic detection data, it is first necessary to correctly identify the species that are detected. The current automatic PAMGUARD Whistle Classifier used to automatically identify whistle detection of cetacean species is modified with the objective to facilitate the use of these detections to estimate cetacean abundance. Given the variability of cetacean sounds within and between species, developing an automated species classifier with a 100% correct classification probability for any species is unfeasible. However, through the examples of two case studies it is shown that large and high quality datasets with which to develop these automatic classifiers increase the probability of creating reliable classifiers with low and precise misclassification probability. Given that misclassification is unavoidable, it is necessary to consider the effect of misclassified detections on the number of observed acoustic calls detected and thus on abundance estimates, and to develop robust methods to cope with these misclassifications. Through both heuristic and Bayesian approaches it is demonstrated that if misclassification probabilities are known or estimated precisely, it is possible to estimate the true number of detected calls accurately and precisely. However, misclassification and uncertainty increase the variance of the estimates. If the true numbers of detections from different species are similar, then a small amount of misclassification between species and a small amount of uncertainty in the probabilities of misclassification does not have a detrimental effect on the overall variance and bias of the estimate. However, if there is a difference in the encounter rate between species calls associated with a large amount of uncertainty in the probabilities of misclassification, then the variance of the estimates becomes larger and the bias increases; this in return increases the variance and the bias of the final abundance estimate. This study despite not bringing perfect results highlights for the first time the importance of dealing with the problem of species misclassification for cetacean if acoustic detections are to be used to estimate abundance of cetaceans.
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Arnold, Klaus, Astrid Ziemann, and Armin Raabe. "Acoustic tomography in comparision to in-situ temperature and wind measurements." Wissenschaftliche Mitteilungen des Leipziger Instituts für Meteorologie ; 22 = Meteorologische Arbeiten aus Leipzig ; 6 (2001), S. 60-68, 2001. https://ul.qucosa.de/id/qucosa%3A15201.
Full textAbstract:
Acoustic travel time tomography is presented as an experimental technique for remote monitoring of areally averaged meteorological quantities as the air temperature and the horizontal wind speed. This ground based remote sensing technique uses the nearly horizontal propagation of sound waves in the atmospheric surface layer. Here the acoustic travel time tomography was applied by measuring the travel time at defined propagation paths between several sound sources and
receivers. The resulting sound speed were used to obtain estimates of the meteorological parameters. A measuring campaign was carried out at the test site in Lindenberg (DWD) to compare the acoustically derived data with conventional systems. These observations demonstrated that on one side the accuracy of the acoustic system is comparable with in-situ measurements and on the other side the temperature was particularly significant overestimated by the standard sensors, e.g. due to the radiation influence.Die Akustische Laufzeittomographie wird als ein experimentelles Verfahren zur Sondierung meteorologischer Parameter, wie z.B. der Lufttemperatur und der horizontalen Windgeschwindigkeit, vorgestellt. Dieses bodengebundene Fernerkundungsverfahren nutzt die horizontale Ausbreitung von Schallwellen in der atmosphärischen Grenzschicht. Hier wird das Verfahren der Laufzeittomographie angewendet, d.h. bei bekannter Weglänge wird die Ausbreitungszeit von ausgesendeten Schallsignalen zwischen mehreren Schallquellen und Empfängern gemessen. Die resultierenden Schallgeschwindigkeitsinformationen werden genutzt, um daraus die entsprechenden meteorologischen Parameter abzuleiten. Auf dem Gelände des Meteorologischen Observatoriums Lindenberg (DWD) wurde eine Messkampagne durchgeführt, um die akustischen Sondierungen mit konventionellen Systemen zu vergleichen. Die Auswertungen zeigen, dass einerseits die Genauigkeit der Akustischen Tomographie vergleichbar mit den konventionellen in-situ Messungen ist und andererseits, dass die Lufttemperatur aufgrund des Strahlungseinflusses bei Messungen mit den üblichen Sensoren zum Teil erheblich überschätzt wird.
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Kahl, Stefan. "Identifying Birds by Sound: Large-scale Acoustic Event Recognition for Avian Activity Monitoring." Universitätsverlag Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A36986.
Full textAbstract:
Automated observation of avian vocal activity and species diversity can be a transformative tool for ornithologists, conservation biologists, and bird watchers to assist in long-term monitoring of critical environmental niches. Deep artificial neural networks have surpassed traditional classifiers in the field of visual recognition and acoustic event classification. Still, deep neural networks require expert knowledge to design, train, and test powerful models. With this constraint and the requirements of future applications in mind, an extensive research platform for automated avian activity monitoring was developed: BirdNET. The resulting benchmark system yields state-of-the-art scores across various acoustic domains and was used to develop expert tools and public demonstrators that can help to advance the democratization of scientific progress and future conservation efforts.Die automatisierte Überwachung der Vogelstimmenaktivität und der Artenvielfalt kann ein revolutionäres Werkzeug für Ornithologen, Naturschützer und Vogelbeobachter sein, um bei der langfristigen Überwachung kritischer Umweltnischen zu helfen. Tiefe künstliche neuronale Netzwerke haben die traditionellen Klassifikatoren im Bereich der visuellen Erkennung und akustische Ereignisklassifizierung übertroffen. Dennoch erfordern tiefe neuronale Netze Expertenwissen, um leistungsstarke Modelle zu entwickeln, trainieren und testen. Mit dieser Einschränkung und unter Berücksichtigung der Anforderungen zukünftiger Anwendungen wurde eine umfangreiche Forschungsplattform zur automatisierten Überwachung der Vogelaktivität entwickelt: BirdNET. Das daraus resultierende Benchmark-System liefert state-of-the-art Ergebnisse in verschiedenen akustischen Bereichen und wurde verwendet, um Expertenwerkzeuge und öffentliche Demonstratoren zu entwickeln, die dazu beitragen können, die Demokratisierung des wissenschaftlichen Fortschritts und zukünftige Naturschutzbemühungen voranzutreiben.
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Havel, Miriam, Gert Hofmann, Dirk Mürbe, and Johan Sundberg. "Contribution of Paranasal Sinuses to the Acoustic Properties of the Nasal Tract." Karger, 2014. https://tud.qucosa.de/id/qucosa%3A71619.
Full textAbstract:
Background: The contribution of the nasal and paranasal cavities to the vocal tract resonator properties is unclear. Here we investigate these resonance phenomena of the sinonasal tract in isolation in a cadaver and compare the results with those gained in a simplified brass tube model. Methods: The resonance characteristics were measured as the response to sine sweep excitation from an earphone. In the brass model the earphone was placed at the closed end and in the cadaver in the epipharynx. The response was picked up by a microphone placed at the open end of the model and at the nostrils, respectively. A shunting cavity with varied volumes was connected to the model and the effects on the response curve were determined. In the cadaver, different conditions with blocked and unblocked middle meatus and sphenoidal ostium were tested. Additionally, infundibulotomy was performed allowing direct access to and selective occlusion of the maxillary ostium. Results: In both the brass model and the cadaver, a baseline condition with no cavities included produced response curves with clear resonance peaks separated by valleys. Marked dips occurred when shunting cavities were attached to the model. The frequencies of these dips decreased with increasing shunting volume. In the cadaver, a marked dip was observed after removing the unilateral occlusion of the middle meatus and the sphenoidal ostium. Another marked dip was detected at low frequency after removal of the occlusion of the maxillary ostium following infundibulotomy. Conclusion: Combining measurements on a simplified nasal model with measurements in a cadaveric sinonasal tract seems a promising method for shedding light on the acoustic properties of the nasal resonator.