Relevant bibliographies by topics / Audio data

Academic literature on the topic 'Audio data'

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Published: 4 June 2021
Last updated: 4 March 2023

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Journal articles on the topic "Audio data"

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Matsunuma, Yasuhiro. "Audio data processing apparatus and audio data distributing apparatus." Journal of the Acoustical Society of America 124, no. 4 (2008): 1903. http://dx.doi.org/10.1121/1.3001094.

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Schuller, Gerald, Matthias Gruhne, and Tobias Friedrich. "Fast Audio Feature Extraction From Compressed Audio Data." IEEE Journal of Selected Topics in Signal Processing 5, no. 6 (October 2011): 1262–71. http://dx.doi.org/10.1109/jstsp.2011.2158802.

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Wylie, F. "Digital audio data compression." Electronics & Communication Engineering Journal 7, no. 1 (February 1, 1995): 5–10. http://dx.doi.org/10.1049/ecej:19950103.

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Seok, Jong Won, and Jin Woo Hong. "Audio watermarking for copyright protection of digital audio data." Electronics Letters 37, no. 1 (2001): 60. http://dx.doi.org/10.1049/el:20010029.

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Patil, Adwait. "Covid Classification Using Audio Data." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (October 31, 2021): 1633–37. http://dx.doi.org/10.22214/ijraset.2021.38675.

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Abstract: Coronavirus outbreak has affected the entire world adversely this project has been developed in order to help common masses diagnose their chances of been covid positive just by using coughing sound and basic patient data. Audio classification is one of the most interesting applications of deep learning. Similar to image data audio data is also stored in form of bits and to understand and analyze this audio data we have used Mel frequency cepstral coefficients (MFCCs) which makes it possible to feed the audio to our neural network. In this project we have used Coughvid a crowdsource dataset consisting of 27000 audio files and metadata of same amount of patients. In this project we have used a 1D Convolutional Neural Network (CNN) to process the audio and metadata. Future scope for this project will be a model that rates how likely it is that a person is infected instead of binary classification. Keywords: Audio classification, Mel frequency cepstral coefficients, Convolutional neural network, deep learning, Coughvid
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BASYSTIUK, Oleh, and Nataliia MELNYKOVA. "MULTIMODAL SPEECH RECOGNITION BASED ON AUDIO AND TEXT DATA." Herald of Khmelnytskyi National University. Technical sciences 313, no. 5 (October 27, 2022): 22–25. http://dx.doi.org/10.31891/2307-5732-2022-313-5-22-25.

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Systems of machine translation of texts from one language to another simulate the work of a human translator. Their performance depends on the ability to understand the grammar rules of the language. In translation, the basic units are not individual words, but word combinations or phraseological units that express different concepts. Only by using them, more complex ideas can be expressed through the translated text. The main feature of machine translation is different length for input and output. The ability to work with different lengths of input and output provides us with the approach of recurrent neural networks. A recurrent neural network (RNN) is a class of artificial neural network that has connections between nodes. In this case, a connection refers to a connection from a more distant node to a less distant node. The presence of connections allows the RNN to remember and reproduce the entire sequence of reactions to one stimulus. From the point of view of programming, such networks are analogous to cyclic execution, and from the point of view of the system, such networks are equivalent to a state machine. RNNs are commonly used to process word sequences in natural language processing. Usually, a hidden Markov model (HMM) and an N-program language model are used to process a sequence of words. Deep learning has completely changed the approach to machine translation. Researchers in the deep learning field has created simple solutions based on machine learning that outperform the best expert systems. In this paper was reviewed the main features of machine translation based on recurrent neural networks. The advantages of systems based on RNN using the sequence-to-sequence model against statistical translation systems are also highlighted in the article. Two machine translation systems based on the sequence-to-sequence model were constructed using Keras and PyTorch machine learning libraries. Based on the obtained results, libraries analysis was done, and their performance comparison.
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Wu, S., J. Huang, D. Huang, and Y. Q. Shi. "Efficiently Self-Synchronized Audio Watermarking for Assured Audio Data Transmission." IEEE Transactions on Broadcasting 51, no. 1 (March 2005): 69–76. http://dx.doi.org/10.1109/tbc.2004.838265.

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Struthers, Allan. "Radioactive Decay: Audio Data Collection." PRIMUS 19, no. 4 (June 12, 2009): 388–95. http://dx.doi.org/10.1080/10511970802238829.

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LIN, RUEI-SHIANG, and LING-HWEI CHEN. "A NEW APPROACH FOR CLASSIFICATION OF GENERIC AUDIO DATA." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 01 (February 2005): 63–78. http://dx.doi.org/10.1142/s0218001405003958.

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The existing audio retrieval systems fall into one of two categories: single-domain systems that can accept data of only a single type (e.g. speech) or multiple-domain systems that offer content-based retrieval for multiple types of audio data. Since a single-domain system has limited applications, a multiple-domain system will be more useful. However, different types of audio data will have different properties, this will make a multiple-domain system harder to be developed. If we can classify audio information in advance, the above problems can be solved. In this paper, we will propose a real-time classification method to classify audio signals into several basic audio types such as pure speech, music, song, speech with music background, and speech with environmental noise background. In order to make the proposed method robust for a variety of audio sources, we use Bayesian decision function for multivariable Gaussian distribution instead of manually adjusting a threshold for each discriminator. The proposed approach can be applied to content-based audio/video retrieval. In the experiment, the efficiency and effectiveness of this method are shown by an accuracy rate of more than 96% for general audio data classification.
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Alderete, John, and Monica Davies. "Investigating Perceptual Biases, Data Reliability, and Data Discovery in a Methodology for Collecting Speech Errors From Audio Recordings." Language and Speech 62, no. 2 (April 6, 2018): 281–317. http://dx.doi.org/10.1177/0023830918765012.

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This work describes a methodology of collecting speech errors from audio recordings and investigates how some of its assumptions affect data quality and composition. Speech errors of all types (sound, lexical, syntactic, etc.) were collected by eight data collectors from audio recordings of unscripted English speech. Analysis of these errors showed that: (i) different listeners find different errors in the same audio recordings, but (ii) the frequencies of error patterns are similar across listeners; (iii) errors collected “online” using on the spot observational techniques are more likely to be affected by perceptual biases than “offline” errors collected from audio recordings; and (iv) datasets built from audio recordings can be explored and extended in a number of ways that traditional corpus studies cannot be.
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Dissertations / Theses on the topic "Audio data"

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Lundberg, Anton. "Data-Driven Procedural Audio : Procedural Engine Sounds Using Neural Audio Synthesis." Thesis, KTH, Datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280132.

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The currently dominating approach for rendering audio content in interactivemedia, such as video games and virtual reality, involves playback of static audiofiles. This approach is inflexible and requires management of large quantities of audio data. An alternative approach is procedural audio, where sound models are used to generate audio in real time from live inputs. While providing many advantages, procedural audio has yet to find widespread use in commercial productions, partly due to the audio produced by many of the proposed models not meeting industry standards. This thesis investigates how procedural audio can be performed using datadriven methods. We do this by specifically investigating how to generate the sound of car engines using neural audio synthesis. Building on a recently published method that integrates digital signal processing with deep learning, called Differentiable Digital Signal Processing (DDSP), our method obtains sound models by training deep neural networks to reconstruct recorded audio examples from interpretable latent features. We propose a method for incorporating engine cycle phase information, as well as a differentiable transient synthesizer. Our results illustrate that DDSP can be used for procedural engine sounds; however, further work is needed before our models can generate engine sounds without undesired artifacts and before they can be used in live real-time applications. We argue that our approach can be useful for procedural audio in more general contexts, and discuss how our method can be applied to other sound sources.
Det i dagsläget dominerande tillvägagångssättet för rendering av ljud i interaktivamedia, såsom datorspel och virtual reality, innefattar uppspelning av statiska ljudfiler. Detta tillvägagångssätt saknar flexibilitet och kräver hantering av stora mängder ljuddata. Ett alternativt tillvägagångssätt är procedurellt ljud, vari ljudmodeller styrs för att generera ljud i realtid. Trots sina många fördelar används procedurellt ljud ännu inte i någon vid utsträckning inom kommersiella produktioner, delvis på grund av att det genererade ljudet från många föreslagna modeller inte når upp till industrins standarder. Detta examensarbete undersöker hur procedurellt ljud kan utföras med datadrivna metoder. Vi gör detta genom att specifikt undersöka metoder för syntes av bilmotorljud baserade på neural ljudsyntes. Genom att bygga på en nyligen publicerad metod som integrerar digital signalbehandling med djupinlärning, kallad Differentiable Digital Signal Processing (DDSP), kan vår metod skapa ljudmodeller genom att träna djupa neurala nätverk att rekonstruera inspelade ljudexempel från tolkningsbara latenta prediktorer. Vi föreslår en metod för att använda fasinformation från motorers förbränningscykler, samt en differentierbar metod för syntes av transienter. Våra resultat visar att DDSP kan användas till procedurella motorljud, men mer arbete krävs innan våra modeller kan generera motorljud utan oönskade artefakter samt innan de kan användas i realtidsapplikationer. Vi diskuterar hur vårt tillvägagångssätt kan vara användbart inom procedurellt ljud i mer generella sammanhang, samt hur vår metod kan tillämpas på andra ljudkällor
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Rydman, Oskar. "Data processing of Controlled Source Audio Magnetotelluric (CSAMT) Data." Thesis, Uppsala universitet, Geofysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387246.

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During this project three distinct methods to improve the data processing of Controlled Source Audio Magnetotellurics (CSAMT) data are implemented and their advantages and disadvantages are discussed. The methods in question are: Detrending the time series in the time domain, instead of detrending in the frequencydomain. Implementation of a coherency test to pinpoint data segments of low quality andremove these data from the calculations. Implementing a method to detect and remove transients from the time series toreduce background noise in the frequency spectra. Both the detrending in time domain and the transient removal shows potential in improvingdata quality even if the improvements are small(both in the (1-10% range). Due totechnical limitations no coherency test was implemented. Overall the processes discussedin the report did improve the data quality and may serve as groundwork for further improvementsto come.
Projektet behandlar tre stycken metoder för att förbättra signalkvaliten hos Controlled Source Audio Magnetotellurics (CSAMT) data, dessa implementeras och deras för- och nackdelar diskuteras. Metoderna som hanteras är: Avlägsnandet av trender från tidsserier i tidsdomänen istället för i frekvensdomänen. Implementationen av ett koherenstest för att identifiera ”dåliga” datasegment ochavlägsna dessa från vidare beräkningar. Implementationen av en metod för att både hitta och avlägsna transienter (dataspikar) från tidsserien för att minska bakgrundsbruset i frekvensspektrat. Både avlägsnandet av trender samt transienter visar positiv inverkan på datakvaliteten,även om skillnaderna är relativt små (båda på ungefär 1-10%). På grund av begränsningarfrån mätdatan kunde inget meningsfullt koherenstest utformas. Överlag har processernasom diskuteras i rapporten förbättrat datakvaliten och kan ses som ett grundarbete förfortsatta förbättringar inom området.
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Levy, Marcel Andrew. "Ringermute an audio data mining toolkit /." abstract and full text PDF (free order & download UNR users only), 2005. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1433402.

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Larsen, Vegard Andreas. "Combining Audio Fingerprints." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8869.

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Large music collections are now more common than ever before. Yet, search technology for music is still in its infancy. Audio fingerprinting is one method that allows searching for music. In this thesis several audio fingerprinting solutions are combined into a single solution to determine if such a combination can yield better results than any of the solutions can separately. The solution is used to find duplicate music files in a personal collection. The results show that applying the weighted root-mean square (WRMS) to the problem most effectively ranked the results in a satisfying manner. It was notably better than the other approaches tried. The WRMS produced 61% more correct matches than the original FDMF solution, and 49% more correct matches than libFooID.

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Morimoto, Norishige. "Techniques for data hiding in audio files." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11422.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.
Includes bibliographical references (leaves 75-76).
by Norishige Morimoto.
M.S.
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Spina, Michelle S. (Michelle Suzanne). "Analysis and transcription of general audio data." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86479.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
Includes bibliographical references (p. 141-147).
by Michelle S. Spina.
Ph.D.
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Gartenlaub, Arie Gal. "Hi fi digital audio tape to SUN workstation transfer system for digital audio data." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1994. http://handle.dtic.mil/100.2/ADA282550.

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Shelley, Michael. "Bay audio repair website & data management application." Click here to view, 2010. http://digitalcommons.calpoly.edu/cscsp/5/.

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Thesis (B.S.)--California Polytechnic State University, 2010.
Project advisor: Franz Kurfess. Title from PDF title page; viewed on Apr. 19, 2010. Includes bibliographical references. Also available on microfiche.
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Lu, Xinyou. "Inversion of controlled-source audio-frequency magnetotelluric data /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/6799.

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Lee, Jong Seo. "RECOMMENDER SYSTEM FOR AUDIO RECORDINGS." DigitalCommons@CalPoly, 2010. https://digitalcommons.calpoly.edu/theses/238.

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Nowadays the largest E-commerce or E-service websites offer millions of products for sale. A Recommender system is defined as software used by such websites for recommending commercial or noncommercial product items to users according to the users’ tastes. In this project, we develop a recommender system for a private multimedia web service company. In particular, we devise three recommendation engines using different data filtering methods – named weighted-average, K-nearest neighbors, and item-based – which are based on collaborative filtering techniques, which work by recording user preferences on items and by anticipating the future likes and dislikes of users by comparing the records, for prediction of user preference. To acquire proper input data for the three engines, we retrieve data from database using three data collection techniques: active filtering, passive filtering, and item-based filtering. For experimental purpose we compare prediction accuracy of those three recommendation engines with the results from each engine and additionally we evaluate the performance of weighted-average method using an empirical analysis approach – a methodology which was devised for verification of predictive accuracy.
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Books on the topic "Audio data"

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Sony. Analog audio: '94-'95 data book. Tokyo: Sony, 1994.

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Sony. Digital audio: '94-'95 data book. Tokyo: Sony, 1994.

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Limited, Hitachi. Hitachi ASSP for audio applications data book. 2nd ed. [Tokyo]: Hitachi, 1993.

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Limited, Hitachi. Hitachi ASSP for audio applications data book. 3rd ed. [Tokyo]: Hitachi, 1995.

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(Indonesia), Perpustakaan Nasional. Pemutakhiran data CD, VCD & kaset koleksi audio visual. [Jakarta]: Perpustakaan Nasional RI, 2008.

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C, Whitaker Jerry, ed. Audio/video protocol handbook: Broadcast standards and reference data. New York: McGraw-Hill, 2002.

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Geological Survey (U.S.), ed. Audio-magnetotelluric data collected in the Beatty, Nevada area. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. Audio-magnetotelluric data collected in the Beatty, Nevada area. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. Audio-magnetotelluric data collected in the Beatty, Nevada area. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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Geological Survey (U.S.), ed. Audio-magnetotelluric data collected in the Beatty, Nevada area. [Reston, Va.]: U.S. Dept. of the Interior, U.S. Geological Survey, 1998.

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Book chapters on the topic "Audio data"

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Salomon, David. "Audio Compression." In Data Compression, 631–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-86092-8_8.

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Schuller, Björn. "Audio Data." In Intelligent Audio Analysis, 23–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36806-6_5.

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Salomon, David, and Giovanni Motta. "Audio Compression." In Handbook of Data Compression, 953–1085. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-903-9_10.

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Sinclair, Jean-Luc. "Audio Data Reduction." In Principles of Game Audio and Sound Design, 276–86. New York, NY : Routledge, 2020.: Focal Press, 2020. http://dx.doi.org/10.4324/9781315184432-12.

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Rumsey, Francis, and Tim McCormick. "Audio Data Reduction." In Sound and Recording, 295–318. 8th ed. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003092919-9.

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Buchanan, William J. "Audio Signals." In Advanced Data Communications and Networks, 129–42. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4419-8670-2_9.

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Salomon, David. "Audio Compression." In A Guide to Data Compression Methods, 241–68. New York, NY: Springer New York, 2002. http://dx.doi.org/10.1007/978-0-387-21708-6_7.

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Buchanan, Bill. "Audio Signals." In Handbook of Data Communications and Networks, 110–21. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-0905-6_10.

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Buchanan, W. J. "Audio Signals." In The Handbook of Data Communications and Networks, 373–84. Boston, MA: Springer US, 2004. http://dx.doi.org/10.1007/978-1-4020-7870-5_20.

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Plankenbühler, Roland, Bernhard Feiten, Thomas Lauterbach, and Ralf Schwalbe. "Data Services and Applications." In Digital Audio Broadcasting, 127–50. Chichester, UK: John Wiley & Sons, Ltd, 2004. http://dx.doi.org/10.1002/0470871431.ch4.

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Conference papers on the topic "Audio data"

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Torazawa, Kenji, Shigekazu Minechika, Seiji Murata, and Yasuhiro Ishii. "Erasable Digital Audio Disc System." In Optical Data Storage. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/ods.1985.tuaa2.

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Following up the popularization of Compact Disc (CD), the epoch of digtial audio is coming really and truly. In the new period the actualization of an erasable optical disc system will be expected greatly.
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Goecke, Potamianos, and Neti. "Noisy audio feature enhancement using audio-visual speech data." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1006170.

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Goecke, Roland, Gerasimos Potamianos, and Chalapathy Neti. "Noisy audio feature enhancement using audio-visual speech data." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745030.

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Sugiura, Toki, Akio Kobayashi, Takehito Utsuro, and Hiromitsu Nishizaki. "Audio Synthesis-based Data Augmentation Considering Audio Event Class." In 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE). IEEE, 2021. http://dx.doi.org/10.1109/gcce53005.2021.9621828.

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Medeiros, Rubem J. V. de, Edmar C. Gurjão, and Joâo M. de Carvalho. "Lossy Audio Compression via Compressed Sensing." In 2010 Data Compression Conference. IEEE, 2010. http://dx.doi.org/10.1109/dcc.2010.88.

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Shah, Sayed Khushal, Zeenat Tariq, and Yugyung Lee. "Audio IoT Analytics for Home Automation Safety." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622587.

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Fitzpatrick, J., and F. Neff. "The Data-Driven Algorithmic Composer." In AM '17: Audio Mostly 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3123514.3123549.

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Gomes, José Alberto, Álvaro Barbosa, and Rui Penha. "An eco-structuralism approach in soundscape (data) composition." In the 9th Audio Mostly. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2636879.2636903.

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Rönnberg, Niklas. "Sonification for Conveying Data and Emotion." In AM '21: Audio Mostly 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3478384.3478387.

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Kumar, Kailash. "Concealing Data in WAVE Audio." In 2021 5th International Conference on Information Systems and Computer Networks (ISCON). IEEE, 2021. http://dx.doi.org/10.1109/iscon52037.2021.9702441.

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Reports on the topic "Audio data"

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Perkins, C., I. Kouvelas, O. Hodson, V. Hardman, M. Handley, J. C. Bolot, A. Vega-Garcia, and S. Fosse-Parisis. RTP Payload for Redundant Audio Data. RFC Editor, September 1997. http://dx.doi.org/10.17487/rfc2198.

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Williams, J. M. Audio-magnetotelluric data collected in the area of Beatty, Nevada. Office of Scientific and Technical Information (OSTI), November 1998. http://dx.doi.org/10.2172/303945.

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Craven, J. A., G. McNeice, B. Powell, R. Koch, I R Annesley, G. Wood, and J. Mwenifumbo. First look at data from a three-dimensional audio-magnetotelluric survey at the McArthur River mining camp, northern Saskatchewan. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2003. http://dx.doi.org/10.4095/214207.

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Tololiu, Kevin Efrain, Arie Kurnianto, and krisztina Csokasi. Audio Intervention for Acute Pain Management - Protocol of Systematic Review and Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, January 2023. http://dx.doi.org/10.37766/inplasy2023.1.0002.

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Review question / Objective: This study aims to investigate, review, and asses existing literature concerning audio intervention to reduce acute pain. Condition being studied: The study will focus on acute pain experienced by adults in any diseases or surgical procedures. Eligibility criteria: This study will exclude papers published older than ten years ago to collect updated data, non RCTs, non-English literature, paper with combined interventions, and papers with an incomplete essential statical value of pain for meta-analysis.
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Decleir, Cyril, Mohand-Saïd Hacid, and Jacques Kouloumdjian. A Database Approach for Modeling and Querying Video Data. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.90.

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Indexing video data is essential for providing content based access. In this paper, we consider how database technology can offer an integrated framework for modeling and querying video data. As many concerns in video (e.g., modeling and querying) are also found in databases, databases provide an interesting angle to attack many of the problems. From a video applications perspective, database systems provide a nice basis for future video systems. More generally, database research will provide solutions to many video issues even if these are partial or fragmented. From a database perspective, video applications provide beautiful challenges. Next generation database systems will need to provide support for multimedia data (e.g., image, video, audio). These data types require new techniques for their management (i.e., storing, modeling, querying, etc.). Hence new solutions are significant. This paper develops a data model and a rule-based query language for video content based indexing and retrieval. The data model is designed around the object and constraint paradigms. A video sequence is split into a set of fragments. Each fragment can be analyzed to extract the information (symbolic descriptions) of interest that can be put into a database. This database can then be searched to find information of interest. Two types of information are considered: (1) the entities (objects) of interest in the domain of a video sequence, (2) video frames which contain these entities. To represent these information, our data model allows facts as well as objects and constraints. We present a declarative, rule-based, constraint query language that can be used to infer relationships about information represented in the model. The language has a clear declarative and operational semantics. This work is a major revision and a consolidation of [12, 13].
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Haver, Samara. Analysis of underwater soundscape conditions at Buck Island Reef National Monument during the COVID-19 pandemic: Focused condition assessment report. National Park Service, October 2022. http://dx.doi.org/10.36967/2294883.

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In partnership with the National Oceanic and Atmospheric Administration and Oregon State University, the National Park Service has been collecting continuous acoustic recordings at a stationary autonomous recorder in Buck Island Reef National Monument since 2016. The audio data were previously analyzed to establish baseline soundscape conditions as well as monitor the acoustic presence of vessels and humpback whales. This report specifically investigates potential changes to the soundscape environment during the onset of the COVID-19 pandemic and the consequent “anthro-pause” when human activities such as tourism and commercial shipping were interrupted by public health guidance. Although major declines of anthropogenic activities were observed in other regions of the world, soundscape conditions in Buck Island Reef National Monument were only minimally impacted during early 2020. Furthermore, in latter months of 2020 and into 2021, vessel movement and related noise levels slightly increased from historic levels. Humpback whale vocalizations were also analyzed for seasonal presence in Buck Island Reef National Monument, revealing a consistent pattern with previously analyzed seasons. Ongoing passive acoustic soundscape monitoring will provide data that can be used to evaluate continued impacts of anthropogenic activity in and near Buck Island Reef National Monument.
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), December 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.
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Kobayashi, K., A. Ogawa, S. Casner, and C. Bormann. RTP Payload Format for 12-bit DAT Audio and 20- and 24-bit Linear Sampled Audio. RFC Editor, January 2002. http://dx.doi.org/10.17487/rfc3190.

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Punjabi, Maitri, Julianne Norman, Lauren Edwards, and Peter Muyingo. Using ACASI to Measure Gender-Based Violence in Ugandan Primary Schools. RTI Press, March 2021. http://dx.doi.org/10.3768/rtipress.2021.rb.0025.2104.

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School-related gender-based violence (SRGBV) remains difficult to measure because of high sensitivity and response bias. However, most SRGBV measurement relies on face-to-face (FTF) survey administration, which is susceptible to increased social desirability bias. Widely used in research on sensitive topics, Audio Computer-Assisted Self-Interview (ACASI) allows subjects to respond to pre-recorded questions on a computerized device, providing respondents with privacy and confidentiality. This brief contains the findings from a large-scale study conducted in Uganda in 2019 where primary grade 3 students were randomly selected to complete surveys using either ACASI or FTF administration. The surveys covered school climate, gender attitudes, social-emotional learning, and experiences of SRGBV. Through this study, we find that although most survey responses were comparable between ACASI and FTF groups, the reporting of experiences of sexual violence differed drastically: 43% of students in the FTF group versus 77% of students in the ACASI group reported experiencing sexual violence in the past school term. We also find that factor structures are similar for data collected with ACASI compared with data collected FTF, though there is weaker evidence for construct validity for both administration modes. We conclude that ACASI is a valuable tool in measuring sensitive sub-topics of SRGBV and should be utilized over FTF administration, although further psychometric testing of these surveys is recommended.
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Pantoja, Tomás, and Signe Flottorp. Does providing healthcare professionals with data about their performance improve their practice? SUPPORT, 2017. http://dx.doi.org/10.30846/170212.

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Audit and feedback is commonly used as a strategy to improve professional practice. It appears logical that healthcare professionals would be prompted to modify their practice if given feedback that their clinical practice was inconsistent with that of their peers or accepted guidelines.
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