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Статті в журналах з теми "Acoustic Scene Analysis"
Terez, Dmitry. "Acoustic scene analysis using microphone arrays." Journal of the Acoustical Society of America 128, no. 4 (October 2010): 2442. http://dx.doi.org/10.1121/1.3508731.
Повний текст джерелаItatani, Naoya, and Georg M. Klump. "Animal models for auditory streaming." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1714 (February 19, 2017): 20160112. http://dx.doi.org/10.1098/rstb.2016.0112.
Повний текст джерелаPark, Sangwook, Woohyun Choi, and Hanseok Ko. "Acoustic scene classification using recurrence quantification analysis." Journal of the Acoustical Society of Korea 35, no. 1 (January 31, 2016): 42–48. http://dx.doi.org/10.7776/ask.2016.35.1.042.
Повний текст джерелаImoto, Keisuke. "Introduction to acoustic event and scene analysis." Acoustical Science and Technology 39, no. 3 (May 1, 2018): 182–88. http://dx.doi.org/10.1250/ast.39.182.
Повний текст джерелаWeisser, Adam, Jörg M. Buchholz, Chris Oreinos, Javier Badajoz-Davila, James Galloway, Timothy Beechey, and Gitte Keidser. "The Ambisonic Recordings of Typical Environments (ARTE) Database." Acta Acustica united with Acustica 105, no. 4 (July 1, 2019): 695–713. http://dx.doi.org/10.3813/aaa.919349.
Повний текст джерелаHou, Yuanbo, and Dick Botteldooren. "Artificial intelligence-based collaborative acoustic scene and event classification to support urban soundscape analysis and classification." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 1 (February 1, 2023): 6466–73. http://dx.doi.org/10.3397/in_2022_0974.
Повний текст джерелаTang, Zhenyu, Nicholas J. Bryan, Dingzeyu Li, Timothy R. Langlois, and Dinesh Manocha. "Scene-Aware Audio Rendering via Deep Acoustic Analysis." IEEE Transactions on Visualization and Computer Graphics 26, no. 5 (May 2020): 1991–2001. http://dx.doi.org/10.1109/tvcg.2020.2973058.
Повний текст джерелаEllison, William T., Adam S. Frankel, David Zeddies, Kathleen J. Vigness Raposa, and Cheryl Schroeder. "Underwater acoustic scene analysis: Exploration of appropriate metrics." Journal of the Acoustical Society of America 124, no. 4 (October 2008): 2433. http://dx.doi.org/10.1121/1.4782511.
Повний текст джерелаMakino, S. "Special Section on Acoustic Scene Analysis and Reproduction." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E91-A, no. 6 (June 1, 2008): 1301–2. http://dx.doi.org/10.1093/ietfec/e91-a.6.1301.
Повний текст джерелаWang, Mou, Xiao-Lei Zhang, and Susanto Rahardja. "An Unsupervised Deep Learning System for Acoustic Scene Analysis." Applied Sciences 10, no. 6 (March 19, 2020): 2076. http://dx.doi.org/10.3390/app10062076.
Повний текст джерелаДисертації з теми "Acoustic Scene Analysis"
Kudo, Hiroaki, Jinji Chen, and Noboru Ohnishi. "Scene Analysis by Clues from the Acoustic Signals." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10426.
Повний текст джерелаFord, Logan H. "Large-scale acoustic scene analysis with deep residual networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123026.
Повний текст джерелаThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-66).
Many of the recent advances in audio event detection, particularly on the AudioSet dataset, have focused on improving performance using the released embeddings produced by a pre-trained model. In this work, we instead study the task of training a multi-label event classifier directly from the audio recordings of AudioSet. Using the audio recordings, not only are we able to reproduce results from prior work, we have also confirmed improvements of other proposed additions, such as an attention module. Moreover, by training the embedding network jointly with the additions, we achieve a mean Average Precision (mAP) of 0.392 and an area under ROC curve (AUC) of 0.971, surpassing the state-of-the-art without transfer learning from a large dataset. We also analyze the output activations of the network and find that the models are able to localize audio events when a finer time resolution is needed. In addition, we use this model in exploring multimodal learning, transfer learning, and realtime sound event detection tasks.
by Logan H. Ford.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Teutsch, Heinz. "Wavefield decomposition using microphone arrays and its application to acoustic scene analysis." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=97902806X.
Повний текст джерелаMcMullan, Amanda R. "Electroencephalographic measures of auditory perception in dynamic acoustic environments." Thesis, Lethbridge, Alta. : University of Lethbridge, Dept. of Neuroscience, c2013, 2013. http://hdl.handle.net/10133/3354.
Повний текст джерелаx, 90 leaves : col. ill. ; 29 cm
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.
Повний текст джерелаCarlo, Diego Di. "Echo-aware signal processing for audio scene analysis." Thesis, Rennes 1, 2020. http://www.theses.fr/2020REN1S075.
Повний текст джерелаMost of audio signal processing methods regard reverberation and in particular acoustic echoes as a nuisance. However, they convey important spatial and semantic information about sound sources and, based on this, recent echo-aware methods have been proposed. In this work we focus on two directions. First, we study the how to estimate acoustic echoes blindly from microphone recordings. Two approaches are proposed, one leveraging on continuous dictionaries, one using recent deep learning techniques. Then, we focus on extending existing methods in audio scene analysis to their echo-aware forms. The Multichannel NMF framework for audio source separation, the SRP-PHAT localization method, and the MVDR beamformer for speech enhancement are all extended to their echo-aware versions
Deleforge, Antoine. "Acoustic Space Mapping : A Machine Learning Approach to Sound Source Separation and Localization." Thesis, Grenoble, 2013. http://www.theses.fr/2013GRENM033/document.
Повний текст джерелаIn this thesis, we address the long-studied problem of binaural (two microphones) sound source separation and localization through supervised leaning. To achieve this, we develop a new paradigm referred as acoustic space mapping, at the crossroads of binaural perception, robot hearing, audio signal processing and machine learning. The proposed approach consists in learning a link between auditory cues perceived by the system and the emitting sound source position in another modality of the system, such as the visual space or the motor space. We propose new experimental protocols to automatically gather large training sets that associates such data. Obtained datasets are then used to reveal some fundamental intrinsic properties of acoustic spaces and lead to the development of a general family of probabilistic models for locally-linear high- to low-dimensional space mapping. We show that these models unify several existing regression and dimensionality reduction techniques, while encompassing a large number of new models that generalize previous ones. The properties and inference of these models are thoroughly detailed, and the prominent advantage of proposed methods with respect to state-of-the-art techniques is established on different space mapping applications, beyond the scope of auditory scene analysis. We then show how the proposed methods can be probabilistically extended to tackle the long-known cocktail party problem, i.e., accurately localizing one or several sound sources emitting at the same time in a real-word environment, and separate the mixed signals. We show that resulting techniques perform these tasks with an unequaled accuracy. This demonstrates the important role of learning and puts forwards the acoustic space mapping paradigm as a promising tool for robustly addressing the most challenging problems in computational binaural audition
Mouterde, Solveig. "Long-range discrimination of individual vocal signatures by a songbird : from propagation constraints to neural substrate." Thesis, Saint-Etienne, 2014. http://www.theses.fr/2014STET4012/document.
Повний текст джерелаIn communication systems, one of the biggest challenges is that the information encoded by the emitter is always modified before reaching the receiver, who has to process this altered information in order to recover the intended message. In acoustic communication particularly, the transmission of sound through the environment is a major source of signal degradation, caused by attenuation, absorption and reflections, all of which lead to decreases in the signal relative to the background noise. How animals deal with the need for exchanging information in spite of constraining conditions has been the subject of many studies either at the emitter or at the receiver's levels. However, a more integrated research about auditory scene analysis has seldom been used, and is needed to address the complexity of this process. The goal of my research was to use a transversal approach to study how birds adapt to the constraints of long distance communication by investigating the information coding at the emitter's level, the propagation-induced degradation of the acoustic signal, and the discrimination of this degraded information by the receiver at both the behavioral and neural levels. Taking into account the everyday issues faced by animals in their natural environment, and using stimuli and paradigms that reflected the behavioral relevance of these challenges, has been the cornerstone of my approach. Focusing on the information about individual identity in the distance calls of zebra finches Taeniopygia guttata, I investigated how the individual vocal signature is encoded, degraded, and finally discriminated, from the emitter to the receiver. This study shows that the individual signature of zebra finches is very resistant to propagation-induced degradation, and that the most individualized acoustic parameters vary depending on distance. Testing female birds in operant conditioning experiments, I showed that they are experts at discriminating between the degraded vocal signatures of two males, and that they can improve their ability substantially when they can train over increasing distances. Finally, I showed that this impressive discrimination ability also occurs at the neural level: we found a population of neurons in the avian auditory forebrain that discriminate individual voices with various degrees of propagation-induced degradation without prior familiarization or training. The finding of such a high-level auditory processing, in the primary auditory cortex, opens a new range of investigations, at the interface of neural processing and behavior
Teki, S. "Cognitive analysis of complex acoustic scenes." Thesis, University College London (University of London), 2013. http://discovery.ucl.ac.uk/1413017/.
Повний текст джерелаWang, Yuxuan. "Supervised Speech Separation Using Deep Neural Networks." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1426366690.
Повний текст джерелаКниги з теми "Acoustic Scene Analysis"
author, Pikrakis Aggelos, ed. Introduction to audio analysis: A MATLAB approach. Kidlington, Oxford: Academic Press is an imprint of Elsevier, 2014.
Знайти повний текст джерелаZur Nieden, Gesa. Symmetries in Spaces, Symmetries in Listening. Edited by Christian Thorau and Hansjakob Ziemer. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190466961.013.16.
Повний текст джерелаPitozzi, Enrico. Body Soundscape. Edited by Yael Kaduri. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199841547.013.43.
Повний текст джерелаKytö, Meri. Soundscapes of Istanbul in Turkish Film Soundtracks. Edited by John Richardson, Claudia Gorbman, and Carol Vernallis. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199733866.013.0028.
Повний текст джерелаЧастини книг з теми "Acoustic Scene Analysis"
de Cheveigné, Alain. "The Cancellation Principle in Acoustic Scene Analysis." In Speech Separation by Humans and Machines, 245–59. Boston, MA: Springer US, 2005. http://dx.doi.org/10.1007/0-387-22794-6_16.
Повний текст джерелаGold, Erica, and Dan McIntyre. "Chapter 4. What the /fʌk/? An acoustic-pragmatic analysis of implicated meaning in a scene from The Wire." In Linguistic Approaches to Literature, 74–91. Amsterdam: John Benjamins Publishing Company, 2019. http://dx.doi.org/10.1075/lal.35.04gol.
Повний текст джерелаSerizel, Romain, Victor Bisot, Slim Essid, and Gaël Richard. "Acoustic Features for Environmental Sound Analysis." In Computational Analysis of Sound Scenes and Events, 71–101. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63450-0_4.
Повний текст джерелаLemaitre, Guillaume, Nicolas Grimault, and Clara Suied. "Acoustics and Psychoacoustics of Sound Scenes and Events." In Computational Analysis of Sound Scenes and Events, 41–67. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63450-0_3.
Повний текст джерелаPham, Lam, Hieu Tang, Anahid Jalali, Alexander Schindler, Ross King, and Ian McLoughlin. "A Low-Complexity Deep Learning Framework For Acoustic Scene Classification." In Data Science – Analytics and Applications, 26–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-36295-9_4.
Повний текст джерелаHuron, David. "Sources and Images." In Voice Leading. The MIT Press, 2016. http://dx.doi.org/10.7551/mitpress/9780262034852.003.0003.
Повний текст джерела"An Auditory Scene Analysis Approach to Monaural Speech Segregation." In Topics in Acoustic Echo and Noise Control, 485–515. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-33213-8_12.
Повний текст джерелаHuron, David. "The Cultural Connection." In Voice Leading. The MIT Press, 2016. http://dx.doi.org/10.7551/mitpress/9780262034852.003.0015.
Повний текст джерелаEpstein, Hugh. "An Audible World." In Hardy, Conrad and the Senses, 139–92. Edinburgh University Press, 2019. http://dx.doi.org/10.3366/edinburgh/9781474449861.003.0005.
Повний текст джерелаPisano, Giusy. "In Praise of the Sound Dissolve: Evanescences, Uncertainties, Fusions, Resonances." In Indefinite Visions, translated by Elise Harris and Martine Beugnet. Edinburgh University Press, 2017. http://dx.doi.org/10.3366/edinburgh/9781474407120.003.0007.
Повний текст джерелаТези доповідей конференцій з теми "Acoustic Scene Analysis"
Imoto, Keisuke, Yasunori Ohishi, Hisashi Uematsu, and Hitoshi Ohmuro. "Acoustic scene analysis based on latent acoustic topic and event allocation." In 2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2013. http://dx.doi.org/10.1109/mlsp.2013.6661957.
Повний текст джерелаImoto, Keisuke, and Nobutaka Ono. "Acoustic scene analysis from acoustic event sequence with intermittent missing event." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7177951.
Повний текст джерелаWang, Weimin, Weiran Wang, Ming Sun, and Chao Wang. "Acoustic Scene Analysis with Multi-Head Attention Networks." In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-1342.
Повний текст джерелаImoto, Keisuke, and Nobutaka Ono. "Online acoustic scene analysis based on nonparametric Bayesian model." In 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016. http://dx.doi.org/10.1109/eusipco.2016.7760396.
Повний текст джерелаKwon, Homin, Harish Krishnamoorthi, Visar Berisha, and Andreas Spanias. "A sensor network for real-time acoustic scene analysis." In 2009 IEEE International Symposium on Circuits and Systems - ISCAS 2009. IEEE, 2009. http://dx.doi.org/10.1109/iscas.2009.5117712.
Повний текст джерелаBasbug, Ahmet Melih, and Mustafa Sert. "Analysis of Deep Neural Network Models for Acoustic Scene Classification." In 2019 27th Signal Processing and Communications Applications Conference (SIU). IEEE, 2019. http://dx.doi.org/10.1109/siu.2019.8806301.
Повний текст джерелаFord, Logan, Hao Tang, François Grondin, and James Glass. "A Deep Residual Network for Large-Scale Acoustic Scene Analysis." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2731.
Повний текст джерелаSharma, Pulkit, Vinayak Abrol, and Anshul Thakur. "ASe: Acoustic Scene Embedding Using Deep Archetypal Analysis and GMM." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1481.
Повний текст джерелаImoto, Keisuke, and Nobutaka Ono. "Spatial-feature-based acoustic scene analysis using distributed microphone array." In 2015 23rd European Signal Processing Conference (EUSIPCO). IEEE, 2015. http://dx.doi.org/10.1109/eusipco.2015.7362480.
Повний текст джерелаImoto, Keisuke. "Acoustic Scene Analysis Using Partially Connected Microphones Based on Graph Cepstrum." In 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018. http://dx.doi.org/10.23919/eusipco.2018.8553385.
Повний текст джерела