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Relevant bibliographies by topics / Acoustic Classification

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Academic literature on the topic 'Acoustic Classification'

Author: Grafiati

Published: 27 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Acoustic Classification"

1

Zheng, Hong Bo, Pin Yan, and Jing Chen. "The Discussion of Acoustic Seabed Sediment Classification Methods." Applied Mechanics and Materials 226-228 (November 2012): 1811–16. http://dx.doi.org/10.4028/www.scientific.net/amm.226-228.1811.

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Acoustic seabed sediment classification method is always important research contents in marine geology and marine acoustics because of its characters of low-cost and high efficiency. At present, there are mainly three types of acoustic seabed sediment classification methods:(1) the echo signal statistical characteristics classification; (2) image texture classification; (3) submarine acoustic parameter inversion method. The principles of anterior two classification methods are similar, which is based on statistics, unknown sediment type can be concluded according to the statistical characteristics of known sediment. There are many usable acoustic equipments and commercial classification software for the two kinds of methods. The third type method is based on suitable seabed sediment model. Seabed acoustic characteristic parameters are inversed and thus seabed sediment can be classified. At present, there are few usable acoustic equipment and commercial classification software for the third method, but it's more accurate than the anterior two classification methods.

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Ma, Ling, Ben Milner, and Dan Smith. "Acoustic environment classification." ACM Transactions on Speech and Language Processing 3, no. 2 (July 2006): 1–22. http://dx.doi.org/10.1145/1149290.1149292.

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Hichem, Hafdaoui, and Benatia Djamel. "Comparative between (LiNbO3) and (LiTaO3) in detecting acoustics microwaves using classification." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 1 (March 1, 2019): 33. http://dx.doi.org/10.11591/ijai.v8.i1.pp33-43.

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Our work is mainly about detecting acoustics microwaves in the type of BAW (Bulk acoustic waves), where we compared between Lithium Niobate (LiNbO3) and Lithium Tantalate (LiTaO3) ,during the propagation of acoustic microwaves in a piezoelectric substrate. In this paper, We have used the classification by Probabilistic Neural Network (PNN) as a means of numerical analysis in which we classify all the values of the real part and the imaginary part of the coefficient attenuation with the acoustic velocity for conclude whichever is the best in utilization for generating bulk acoustic waves.This study will be very interesting in modeling and realization of acoustic microwaves devices (ultrasound) based on the propagation of acoustic microwaves.

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Choi. "Acoustic Target of Interest Tracking Algorithm Using Classification Feedback." Journal Of The Acoustical Society Of Korea 33, no. 4 (2014): 225. http://dx.doi.org/10.7776/ask.2014.33.4.225.

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Martin, Linda V., Timothy K. Stanton, Peter H. Wiebe, and James F. Lynch. "Acoustic classification of zooplankton." Journal of the Acoustical Society of America 98, no. 5 (November 1995): 2881. http://dx.doi.org/10.1121/1.413130.

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Wilson, Joshua D., and Nicholas C. Makris. "Ocean acoustic hurricane classification." Journal of the Acoustical Society of America 119, no. 1 (January 2006): 168–81. http://dx.doi.org/10.1121/1.2130961.

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Martin, L. "Acoustic classification of zooplankton." ICES Journal of Marine Science 53, no. 2 (April 1996): 217–24. http://dx.doi.org/10.1006/jmsc.1996.0025.

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Nooralahiyan, A. Y., H. R. Kirby, and D. McKeown. "Vehicle classification by acoustic signature." Mathematical and Computer Modelling 27, no. 9-11 (May 1998): 205–14. http://dx.doi.org/10.1016/s0895-7177(98)00060-0.

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Leonetti, Marc C., and Edward A. Hand. "Acoustic classification using fuzzy sets." Journal of the Acoustical Society of America 92, no. 4 (October 1992): 2418–19. http://dx.doi.org/10.1121/1.404644.

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Malkin, R. A., and D. Alexandrou. "Acoustic classification of abyssopelagic animals." IEEE Journal of Oceanic Engineering 18, no. 1 (1993): 63–72. http://dx.doi.org/10.1109/48.211495.

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Dissertations / Theses on the topic "Acoustic Classification"

1

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 les
sortides 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, &ldquo
visual&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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Books on the topic "Acoustic Classification"

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Traykovski, Linda V. Martin. Acoustic classification of zooplankton. Woods Hole, Mass: Massachusetts Institute of Technology, Woods Hole Oceanographic Institution, Joint Program in Oceanography/Applied Ocean Science and Engineering, 1998.

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Acoustic emission, microseismic activity. Lisse: Balkema, 2003.

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Wichert, Terry S. Feature based neural network acoustic transient signal classification. Monterey, Calif: Naval Postgraduate School, 1993.

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Flowers, Nicholas. Remote classification of sea bed material using backscattered acoustic signals. Birmingham: University of Birmingham, 1987.

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Gabsdil, Malte. Automatic classification of speech recognition hypotheses using acoustic and pragmatic features. Saarbrücken: DFKI & Universität des Saarlandes, 2005.

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

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Tang, Xiaoou. Dominant run-length method for image classification. [Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

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Tang, Xiaoou. Dominant run-length method for image classification. [Woods Hole, Mass: Woods Hole Oceanographic Institution, 1997.

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Tonn, Joerg-Christian, and Douglas Kondziolka. Tumours of the cranial nerves. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199651870.003.0010.

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‘Tumours of the cranial nerves’ describes diagnosis and management for the most common tumours such as vestibular (acoustic) schwannomas as well as for rare entities such as optic nerve sheath meningioma and esthesioneuroblastoma. It reviews the current data concerning epidemiology and the grading systems according to the World Health Organization classification of central nervous system tumours and describes refined magnetic resonance imaging techniques for differential diagnosis. Special emphasis is placed on the discussion of specific therapeutic modalities such as microsurgery, radiotherapy, as well as stereotactic radiosurgery, either alone or in combination. The focus of the differential therapeutic considerations is to provide personalized approaches in order to attain maximal efficacy with preservation of neurological function.

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Book chapters on the topic "Acoustic Classification"

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Hashimoto, Sho. "Classification of Vestibular Schwannoma (Acoustic Neuroma)." In Acoustic Neuroma, 13–16. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_3.

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Moffat, David A. "Moffat Classification of Facial Nerve Function." In Acoustic Neuroma, 73–78. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_13.

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Sekiya, Tetsuji, and Shigeharu Suzuki. "A Classification System for Vestibular Schwannomas." In Acoustic Neuroma, 45–48. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_9.

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Magnan, Jacques P. Y. "Surgical Classification and Predictive Factors in Acoustic Neuromas." In Acoustic Neuroma, 39–43. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_8.

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Murakami, Shingo, Nobuhiro Watanabe, and Sotaro Kamei. "New Classification of Postoperative Hearing Results Following Acoustic Neuroma Surgery." In Acoustic Neuroma, 117–20. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_20.

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Temko, Andrey, Climent Nadeu, Dušan Macho, Robert Malkin, Christian Zieger, and Maurizio Omologo. "Acoustic Event Detection and Classification." In Computers in the Human Interaction Loop, 61–73. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-054-8_7.

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Patole, Rashmika, and Priti Rege. "Acoustic Classification of Bird Species." In Lecture Notes in Electrical Engineering, 313–19. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8391-9_23.

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Lourens, J. G. "Classification of Ships Using Underwater Radiated Noise." In Underwater Acoustic Data Processing, 591–96. Dordrecht: Springer Netherlands, 1989. http://dx.doi.org/10.1007/978-94-009-2289-1_66.

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Ishikawa, Kazuo, Zhiwei Cao, Yan Wang, Katsumi Monoo, and Nobuyuki Yasui. "Classification of Tumor Size from the Point of View of Functional Preservation, Based Upon Our 53 Surgical Cases with Acoustic Neuroma." In Acoustic Neuroma, 29–34. Tokyo: Springer Japan, 2003. http://dx.doi.org/10.1007/978-4-431-53942-1_6.

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Ren, Chunxia, and Shengchen Li. "Two-Stage Classification Learning for Open Set Acoustic Scene Classification." In Proceedings of the 8th Conference on Sound and Music Technology, 124–33. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1649-5_11.

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Conference papers on the topic "Acoustic Classification"

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Mayorga, Pedro, Julio Valdez, Vesna Zeljkovic, Christopher Druzgalski, and Monceni A. Perez. "Cardiopulmonary acoustic events classification." In 2016 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2016. http://dx.doi.org/10.1109/hpcsim.2016.7568381.

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Saki, Fatemeh, Yinyi Guo, Cheng-Yu Hung, Lae-hoon Kim, Manyu Deshpande, Sunkuk Moon, Eunjeong Koh, and Erik Visser. "Open-set Evolving Acoustic Scene Classification System." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/en2t-9m14.

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Remaggi, Luca, Hansung Kim, Philip J. B. Jackson, Filippo Maria Fazi, and Adrian Hilton. "Acoustic Reflector Localization and Classification." In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2018. http://dx.doi.org/10.1109/icassp.2018.8462146.

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Felipe, Gustavo Zanoni, Yandre Maldonado, Gomes da Costa, and Lucas Georges Helal. "Acoustic scene classification using spectrograms." In 2017 36th International Conference of the Chilean Computer Science Society (SCCC). IEEE, 2017. http://dx.doi.org/10.1109/sccc.2017.8405119.

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Sampath, D. Y. K., and G. D. S. P. Wimalarathne. "Obstacle classification through acoustic echolocation." In 2015 International Conference on Estimation, Detection and Information Fusion (ICEDIF). IEEE, 2015. http://dx.doi.org/10.1109/icedif.2015.7280147.

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Au-Yeung, Justin, Mahesh K. Banavar, and Vanitha M. "Room Classification using Acoustic Signals." In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). IEEE, 2020. http://dx.doi.org/10.1109/ic-etite47903.2020.91.

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Nurzynski, Jacek. "New Acoustic Classification Scheme for Residential Buildings in Poland." In 2018 Joint Conference - Acoustics. IEEE, 2018. http://dx.doi.org/10.1109/acoustics.2018.8502338.

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Wilkinghoff, Kevin, and Frank Kurth. "Open-Set Acoustic Scene Classification with Deep Convolutional Autoencoders." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/340j-wd27.

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Huang, Jonathan, Hong Lu, Paulo Lopez Meyer, Hector Cordourier, and Juan Del Hoyo Ontiveros. "Acoustic Scene Classification Using Deep Learning-based Ensemble Averaging." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/8rd2-g787.

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Koutini, Khaled, Hamid Eghbal-zadeh, and Gerhard Widmer. "Receptive-Field-Regularized CNN Variants for Acoustic Scene Classification." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/cjd9-kc43.

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Reports on the topic "Acoustic Classification"

1

Thammakhoune, Ned B., and Stephen W. Lang. Long Range Acoustic Classification. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada393792.

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Eom, K., M. Wellman, N. Srour, D. Hillis, and R. Chellappa. Acoustic Target Classification Using Multiscale Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1998. http://dx.doi.org/10.21236/ada358579.

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Stanton, Timothy K., and Peter H. Wiebe. Acoustic Scattering Classification of Zooplankton and Microstructure. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada609882.

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Stanton, Timothy K., and Peter H. Wiebe. Acoustic Scattering Classification of Zooplankton and Microstructure. Fort Belvoir, VA: Defense Technical Information Center, October 2003. http://dx.doi.org/10.21236/ada418128.

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Stanton, Timothy K., and Dezhang Chu. Acoustic Resonance Classification of Swimbladder-Bearing Fish. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada525356.

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Stanton, Timothy K., Dezhang Chu, and J. M. Jech. Acoustic Resonance Classification of Swimbladder-Bearing Fish. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573418.

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Stanton, Timothy K., and Peter H. Wiebe. Acoustic Scattering Classification of Zooplankton and Microstructure. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada628843.

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Stanton, Timothy K., and Peter H. Wiebe. Acoustic Scattering Classification of Zooplankton and Microstructure. Fort Belvoir, VA: Defense Technical Information Center, September 2001. http://dx.doi.org/10.21236/ada626242.

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Goldman, Geoffrey H., Ronald M. Holben, and Guy L. Williams. Performance Metrics for Acoustic Classification of Weapons Fire. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada570174.

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McLaughlin, Jack, Scott Philips, and James Pitton. Perceptually-Driven Signal Analysis for Acoustic Event Classification. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada476810.

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