Relevant bibliographies by topics / Computational auditory scene analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Computational auditory scene analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Computational auditory scene analysis"

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Brown, Guy J., and Martin Cooke. "Computational auditory scene analysis." Computer Speech & Language 8, no. 4 (1994): 297–336. http://dx.doi.org/10.1006/csla.1994.1016.

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Alain, Claude, and Lori J. Bernstein. "Auditory Scene Analysis." Music Perception 33, no. 1 (2015): 70–82. http://dx.doi.org/10.1525/mp.2015.33.1.70.

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Albert Bregman’s (1990) book Auditory Scene Analysis: The Perceptual Organization of Sound has had a tremendous impact on research in auditory neuroscience. Here, we outline some of the accomplishments. This review is not meant to be exhaustive, but rather aims to highlight milestones in the brief history of auditory neuroscience. The steady increase in neuroscience research following the book’s pivotal publication has advanced knowledge about how the brain forms representations of auditory objects. This research has far-reaching societal implications on health and quality of life. For instanc
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Brown, Guy J. "Computational auditory scene analysis: A representational approach." Journal of the Acoustical Society of America 94, no. 4 (1993): 2454. http://dx.doi.org/10.1121/1.407441.

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Lewicki, Michael S., Bruno A. Olshausen, Annemarie Surlykke, and Cynthia F. Moss. "Computational issues in natural auditory scene analysis." Journal of the Acoustical Society of America 137, no. 4 (2015): 2249. http://dx.doi.org/10.1121/1.4920202.

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Niessen, Maria E., Ronald A. Van Elburg, Dirkjan J. Krijnders, and Tjeerd C. Andringa. "A computational model for auditory scene analysis." Journal of the Acoustical Society of America 123, no. 5 (2008): 3301. http://dx.doi.org/10.1121/1.2933719.

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Nakadai, Kazuhiro, and Hiroshi G. Okuno. "Robot Audition and Computational Auditory Scene Analysis." Advanced Intelligent Systems 2, no. 9 (2020): 2000050. http://dx.doi.org/10.1002/aisy.202000050.

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McMullin, Margaret A., Rohit Kumar, Nathan C. Higgins, Brian Gygi, Mounya Elhilali, and Joel S. Snyder. "Preliminary Evidence for Global Properties in Human Listeners During Natural Auditory Scene Perception." Open Mind 8 (2024): 333–65. http://dx.doi.org/10.1162/opmi_a_00131.

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Abstract Theories of auditory and visual scene analysis suggest the perception of scenes relies on the identification and segregation of objects within it, resembling a detail-oriented processing style. However, a more global process may occur while analyzing scenes, which has been evidenced in the visual domain. It is our understanding that a similar line of research has not been explored in the auditory domain; therefore, we evaluated the contributions of high-level global and low-level acoustic information to auditory scene perception. An additional aim was to increase the field’s ecologica
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Godsmark, Darryl, and Guy J. Brown. "A blackboard architecture for computational auditory scene analysis." Speech Communication 27, no. 3-4 (1999): 351–66. http://dx.doi.org/10.1016/s0167-6393(98)00082-x.

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Kondo, Hirohito M., Anouk M. van Loon, Jun-Ichiro Kawahara, and Brian C. J. Moore. "Auditory and visual scene analysis: an overview." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1714 (2017): 20160099. http://dx.doi.org/10.1098/rstb.2016.0099.

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We perceive the world as stable and composed of discrete objects even though auditory and visual inputs are often ambiguous owing to spatial and temporal occluders and changes in the conditions of observation. This raises important questions regarding where and how ‘scene analysis’ is performed in the brain. Recent advances from both auditory and visual research suggest that the brain does not simply process the incoming scene properties. Rather, top-down processes such as attention, expectations and prior knowledge facilitate scene perception. Thus, scene analysis is linked not only with the
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Cooke, M. P., and G. J. Brown. "Computational auditory scene analysis: Exploiting principles of perceived continuity." Speech Communication 13, no. 3-4 (1993): 391–99. http://dx.doi.org/10.1016/0167-6393(93)90037-l.

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Dissertations / Theses on the topic "Computational auditory scene analysis"

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Ellis, Daniel Patrick Whittlesey. "Prediction-driven computational auditory scene analysis." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11006.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1996.<br>Includes bibliographical references (p. 173-180).<br>by Daniel P.W. Ellis.<br>Ph.D.
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Delmotte, Varinthira Duangudom. "Computational auditory saliency." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45888.

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The objective of this dissertation research is to identify sounds that grab a listener's attention. These sounds that draw a person's attention are sounds that are considered salient. The focus here will be on investigating the role of saliency in the auditory attentional process. In order to identify these salient sounds, we have developed a computational auditory saliency model inspired by our understanding of the human auditory system and auditory perception. By identifying salient sounds we can obtain a better understanding of how sounds are processed by the auditory system, and in par
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Shao, Yang. "Sequential organization in computational auditory scene analysis." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1190127412.

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Brown, Guy Jason. "Computational auditory scene analysis : a representational approach." Thesis, University of Sheffield, 1992. http://etheses.whiterose.ac.uk/2982/.

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This thesis addresses the problem of how a listener groups together acoustic components which have arisen from the same environmental event, a phenomenon known as auditory scene analysis. A computational model of auditory scene analysis is presented, which is able to separate speech from a variety of interfering noises. The model consists of four processing stages. Firstly, the auditory periphery is simulated by a bank of bandpass filters and a model of inner hair cell function. In the second stage, physiologically-inspired models of higher auditory organization - aiditory maps - are used to p
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Srinivasan, Soundararajan. "Integrating computational auditory scene analysis and automatic speech recognition." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1158250036.

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Narayanan, Arun. "Computational auditory scene analysis and robust automatic speech recognition." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1401460288.

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Unnikrishnan, Harikrishnan. "AUDIO SCENE SEGEMENTATION USING A MICROPHONE ARRAY AND AUDITORY FEATURES." UKnowledge, 2010. http://uknowledge.uky.edu/gradschool_theses/622.

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Auditory stream denotes the abstract effect a source creates in the mind of the listener. An auditory scene consists of many streams, which the listener uses to analyze and understand the environment. Computer analyses that attempt to mimic human analysis of a scene must first perform Audio Scene Segmentation (ASS). ASS find applications in surveillance, automatic speech recognition and human computer interfaces. Microphone arrays can be employed for extracting streams corresponding to spatially separated sources. However, when a source moves to a new location during a period of silence, such
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Nakatani, Tomohiro. "Computational Auditory Scene Analysis Based on Residue-driven Architecture and Its Application to Mixed Speech Recognition." 京都大学 (Kyoto University), 2002. http://hdl.handle.net/2433/149754.

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Javadi, Ailar. "Bio-inspired noise robust auditory features." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44801.

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The purpose of this work is to investigate a series of biologically inspired modifications to state-of-the-art Mel- frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition results. We have provided recommendations to improve speech recognition results de- pending on signal-to-noise ratio levels of input signals. This work has been motivated by noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a spatial derivative step is used to sharpen the results, followed by an envelope detector (recti
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Melih, Kathy, and n/a. "Audio Source Separation Using Perceptual Principles for Content-Based Coding and Information Management." Griffith University. School of Information Technology, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20050114.081327.

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The information age has brought with it a dual problem. In the first place, the ready access to mechanisms to capture and store vast amounts of data in all forms (text, audio, image and video), has resulted in a continued demand for ever more efficient means to store and transmit this data. In the second, the rapidly increasing store demands effective means to structure and access the data in an efficient and meaningful manner. In terms of audio data, the first challenge has traditionally been the realm of audio compression research that has focused on statistical, unstructured audio represent
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Books on the topic "Computational auditory scene analysis"

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F, Rosenthal David, and Okuno Hiroshi G, eds. Computational auditory scene analysis. Lawrence Erlbaum Associates, 1998.

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Ellis, Daniel P. W. Prediction-driven computational auditory scene analysis. [publisher not identified], 1996.

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Lerch, Alexander. Audio content analysis: An introduction. Wiley, 2012.

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Wang, Wenwu. Machine audition: Principles, algorithms, and systems. Information Science Reference, 2010.

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Rowe, Robert. Interactive music systems: Machine listening and composing. MIT Press, 1993.

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author, Pikrakis Aggelos, ed. Introduction to audio analysis: A MATLAB approach. Academic Press is an imprint of Elsevier, 2014.

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McDonald, Kelly Loreen. The role of harmonicity and location cues in auditory scene analysis. National Library of Canada, 2003.

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Emerit, Sibylle, and Sylvain Perrot. Le paysage sonore de l'antiquité: Méthodologie, historiographie et perspectives : actes de la journée d'études tenue à l'École française de Rome, le 7 janvier 2013. Institut Française d'Archéologie Orientale, 2015.

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Rosenthal, David F., Hiroshi G. Okuno, Hiroshi Okuno, and David Rosenthal, eds. Computational Auditory Scene Analysis. CRC Press, 2020. http://dx.doi.org/10.1201/9781003064183.

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Wang, DeLiang, and Guy J. Brown. Computational Auditory Scene Analysis. IEEE, 2006. http://dx.doi.org/10.1109/9780470043387.

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Book chapters on the topic "Computational auditory scene analysis"

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Mellinger, David K., and Bernard M. Mont-Reynaud. "Scene Analysis." In Auditory Computation. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4070-9_7.

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Brown, Guy J. "Physiological Models of Auditory Scene Analysis." In Computational Models of the Auditory System. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5934-8_8.

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Narayanan, Arun, and Deliang Wang. "Computational Auditory Scene Analysis and Automatic Speech Recognition." In Techniques for Noise Robustness in Automatic Speech Recognition. John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118392683.ch16.

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Kashino, Makio, Eisuke Adachi, and Haruto Hirose. "A Computational Approach to the Dynamic Aspects of Primitive Auditory Scene Analysis." In Advances in Experimental Medicine and Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-1590-9_57.

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Hummersone, Christopher, Toby Stokes, and Tim Brookes. "On the Ideal Ratio Mask as the Goal of Computational Auditory Scene Analysis." In Blind Source Separation. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55016-4_12.

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Wang, DeLiang. "Computational Scene Analysis." In Challenges for Computational Intelligence. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71984-7_8.

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Leibold, Lori J. "Development of Auditory Scene Analysis and Auditory Attention." In Human Auditory Development. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1421-6_5.

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Carlyon, Robert P., Sarah K. Thompson, Antje Heinrich, et al. "Objective Measures of Auditory Scene Analysis." In The Neurophysiological Bases of Auditory Perception. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-5686-6_47.

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Stowell, Dan. "Computational Bioacoustic Scene Analysis." In Computational Analysis of Sound Scenes and Events. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63450-0_11.

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Mountain, David C., and Allyn E. Hubbard. "Computational Analysis of Hair Cell and Auditory Nerve Processes." In Auditory Computation. Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-4070-9_4.

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Conference papers on the topic "Computational auditory scene analysis"

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Venkatesh, K., K. Venkata Hitesh Kumar Chowdary, M. Krishna Sairam, M. Santhosh Sai Ram Kumar Reddy, and M. Anil Kumar. "Forensic Flow: A Detailed Crime Scene Detection and Analysis Using Machine Learning." In 2025 International Conference on Computational Robotics, Testing and Engineering Evaluation (ICCRTEE). IEEE, 2025. https://doi.org/10.1109/iccrtee64519.2025.11053017.

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Pile, Santa, Oleg Lesota, Silvan David Peter, Christina Humer, and Martin Di Gasser. "Spin-Wave Voices: Sonification of Nanoscale Spin Waves as an Engagement and Research Tool." In ICAD 2024: The 29th International Conference on Auditory Display. International Community for Auditory Display, 2024. http://dx.doi.org/10.21785/icad2024.024.

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Magnonics is an emerging research field that addresses the use of spin waves (magnons), purely magnetic waves, for information transport and processing. Spin waves are a potential replacement for electric current in novel computational devices that would make them more compact and energy effcient. The field is yet little known, even among physicists. Additionally, with the development of new measuring techniques and computational physics, the obtained magnetic data becomes more complex, in some cases including 3D vector fields and time-resolution. This work presents an approach to the audio-vi
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Brown, Guy J., and Martin P. Cooke. "A computational model of auditory scene analysis." In 2nd International Conference on Spoken Language Processing (ICSLP 1992). ISCA, 1992. http://dx.doi.org/10.21437/icslp.1992-172.

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Yang Shao and DeLiang Wang. "Robust speaker identification using auditory features and computational auditory scene analysis." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517928.

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Tu, Ming, Xiang Xie, and Xingyu Na. "Computational Auditory Scene Analysis Based Voice Activity Detection." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.147.

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Kawamoto, Mitsuru, and Takuji Hamamoto. "Building Health Monitoring Using Computational Auditory Scene Analysis." In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2020. http://dx.doi.org/10.1109/dcoss49796.2020.00033.

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Larigaldie, Nathanael, and Ulrik Beierholm. "Explaining Human Auditory Scene Analysis Through Bayesian Clustering." In 2019 Conference on Cognitive Computational Neuroscience. Cognitive Computational Neuroscience, 2019. http://dx.doi.org/10.32470/ccn.2019.1227-0.

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Okuno, Hiroshi G., Tetsuya Ogata, and Kazunori Komatani. "Robot Audition from the Viewpoint of Computational Auditory Scene Analysis." In International Conference on Informatics Education and Research for Knowledge-Circulating Society (icks 2008). IEEE, 2008. http://dx.doi.org/10.1109/icks.2008.10.

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Srinivasan, Soundararajan, Yang Shao, Zhaozhang Jin, and DeLiang Wang. "A computational auditory scene analysis system for robust speech recognition." In Interspeech 2006. ISCA, 2006. http://dx.doi.org/10.21437/interspeech.2006-19.

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Kawamoto, Mitsuru. "Sound-environment monitoring technique based on computational auditory scene analysis." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081664.

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Reports on the topic "Computational auditory scene analysis"

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Shao, Yang, Soundararajan Srinivasan, Zhaozhang Jin, and DeLiang Wang. A Computational Auditory Scene Analysis System for Speech Segregation and Robust Speech Recognition. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ad1001212.

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Lazzaro, John, and John Wawrzynek. Silicon Models for Auditory Scene Analysis. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada327239.

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McKinnon, Mark, and Daniel Madryzkowski. Literature Review to Support the Development of a Database of Contemporary Material Properties for Fire Investigation Analysis. UL Firefighter Safety Research Institute, 2020. http://dx.doi.org/10.54206/102376/wmah2173.

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The NIJ Technology Working Group’s Operational Requirements (TWG ORs) for Fire and Arson Investigation have included several scientific research needs that require knowledge of the thermophysical properties of materials that are common in the built environment, and therefore likely to be involved in a fire scene. The specific areas of research include: adequate materials property data inputs for accurate computer models, understanding the effect of materials properties on the development and interpretation of fire patterns, and evaluation of incident heat flux profiles to walls and neighboring
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