Relevant bibliographies by topics / Acoustic Unit Discovery

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Academic literature on the topic 'Acoustic Unit Discovery'

Author: Grafiati
Published: 28 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Acoustic Unit Discovery"

1

Ondel, Lucas, Lukaš Burget, and Jan Černocký. "Variational Inference for Acoustic Unit Discovery." Procedia Computer Science 81 (2016): 80–86. http://dx.doi.org/10.1016/j.procs.2016.04.033.

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Lee, Chia-ying, Timothy J. O’Donnell, and James Glass. "Unsupervised Lexicon Discovery from Acoustic Input." Transactions of the Association for Computational Linguistics 3 (December 2015): 389–403. http://dx.doi.org/10.1162/tacl_a_00146.

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We present a model of unsupervised phonological lexicon discovery—the problem of simultaneously learning phoneme-like and word-like units from acoustic input. Our model builds on earlier models of unsupervised phone-like unit discovery from acoustic data (Lee and Glass, 2012), and unsupervised symbolic lexicon discovery using the Adaptor Grammar framework (Johnson et al., 2006), integrating these earlier approaches using a probabilistic model of phonological variation. We show that the model is competitive with state-of-the-art spoken term discovery systems, and present analyses exploring the model’s behavior and the kinds of linguistic structures it learns.
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Shum, Stephen H., David F. Harwath, Najim Dehak, and James R. Glass. "On the Use of Acoustic Unit Discovery for Language Recognition." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 9 (September 2016): 1665–76. http://dx.doi.org/10.1109/taslp.2016.2582260.

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HECK, Michael, Sakriani SAKTI, and Satoshi NAKAMURA. "Learning Supervised Feature Transformations on Zero Resources for Improved Acoustic Unit Discovery." IEICE Transactions on Information and Systems E101.D, no. 1 (2018): 205–14. http://dx.doi.org/10.1587/transinf.2017edp7175.

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Razavi, Marzieh, Ramya Rasipuram, and Mathew Magimai.-Doss. "Towards weakly supervised acoustic subword unit discovery and lexicon development using hidden Markov models." Speech Communication 96 (February 2018): 168–83. http://dx.doi.org/10.1016/j.specom.2017.11.011.

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Pandia, Karthik, and Hema A. Murthy. "Acoustic unit discovery using transient and steady-state regions in speech and its applications." Journal of Phonetics 88 (September 2021): 101081. http://dx.doi.org/10.1016/j.wocn.2021.101081.

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McLerie, M. K., A. M. Tait, and M. J. Sayers. "THE YAMMADERRY, COWLE AND ROLLER DISCOVERIES IN THE BARROW SUB-BASIN, WESTERN AUSTRALIA." APPEA Journal 31, no. 1 (1991): 32. http://dx.doi.org/10.1071/aj90004.

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The TP/3 Part I permit in the Barrow Sub-basin has been held by WAPET since 1952. Improvements in seismic quality and oilfield economics in the early 1980s resulted in the 1985 Saladin oil discovery, which subsequently led to the Yammaderry, Cowle and Roller discoveries.Yammaderry-1, drilled in 1988, encountered 16.5 m of gas capping a nine metre oil column. In 1989, Cowle-1 penetrated a 14 m oil column and tested at 1016 m3 (6390 BBL) of oil per day. Roller-1, drilled in 1990, encountered six metres of gas capping nine metres of oil and tested at 866 m3 (5450 BBL) of oil per day. Roller-2, deviated downdip to find the oil/water contact, proved an 18 m oil column, confirmed later by Roller-4.Early Cretaceous Barrow Group deltaic sandstones are the reservoirs for the Saladin, Yammaderry, Cowle and Roller oil fields. The Barrow Group is overlain by the Mar- die Greensand, the basal unit of the Muderong Shale which forms the regional seal. The transitional acoustic character of the Mardie Greensand and its thickness, variable fluid saturation and lithology, cause problems in picking a top Barrow Group event. Vertical Seismic Profiles acquired in the Yammaderry, Cowle and Roller wells have helped tie the wells to the seismic data.With Saladin on stream, and Yammaderry and Cowle under development, a major seismic survey was completed in late 1990 to delineate Roller and to detail prospects for future drilling in the revitalised TP / 3 Part 1 permit.
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Carbaugh-Rutland, A., J. Have Rasmussen, B. Sterba-Boatwright, and A. Širović. "Geographically distinct blue whale song variants in the Northeast Pacific." Endangered Species Research 46 (September 9, 2021): 19–33. http://dx.doi.org/10.3354/esr01145.

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The Northeast Pacific (NEP) population of blue whales Balaenoptera musculus musculus is currently managed as a single stock. We investigated the fine-scale frequency characteristics of 1 NEP blue whale song unit, the B call. We analyzed B calls from passive acoustic data collected between 2010 and 2013 at 2 low-latitude sites, Palmyra Atoll and the Hawaiian Islands, and 3 higher-latitude sites, off southern California, off Washington state and in the Gulf of Alaska. Frequency measurements were extracted along the contour of the third harmonic from each call, and data from each region were compared. Calls from the Gulf of Alaska and Hawai‘i presented a downshift in frequency, beginning just past the midway point of the contour, which was not present in calls recorded from southern California or Palmyra Atoll. Calls from Washington displayed intermediate characteristics between those from the other 2 high-latitude sites. Cluster analysis resulted in consistent grouping of call contours from Washington and southern California, in what we termed the NEP B1 variant, while contours from Hawai‘i and the Gulf of Alaska were grouped together, as a NEP B2 variant. Frequency differences were also observed among the variants; the Gulf of Alaska displayed the highest frequency on average, followed by Washington, then southern California. Consistent with other studies, a yearly decline in the frequency of B calls was also observed. This discovery of at least 2 geographically distinct variants provides the first evidence of vocally distinct subpopulations within the NEP, indicating the possibility of a need for finer-scale population segmentation.
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Quy, Thang Bui, and Jong-Myon Kim. "Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features." Sensors 21, no. 2 (January 7, 2021): 367. http://dx.doi.org/10.3390/s21020367.

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This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.
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Quy, Thang Bui, and Jong-Myon Kim. "Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features." Sensors 21, no. 2 (January 7, 2021): 367. http://dx.doi.org/10.3390/s21020367.

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This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.
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Books on the topic "Acoustic Unit Discovery"

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Bever, Thomas G. The Unity of Consciousness and the Consciousness of Unity. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190464783.003.0005.

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Every language-learning child eventually automatically segments the organization of word sequences into natural units. Within the natural units, processing of normal conversation reveals a disconnect between listener’s representation of the sound and meaning of utterances. A compressed or absent word at a point early in a sequence is unintelligible until later acoustic information, yet listeners think they perceived the earlier sounds and their interpretation as they were heard. This discovery has several implications: Our conscious unified experience of language as we hear and simultaneously interpret it is partly reconstructed in time-suspended “psychological moments”; the “poverty of the stimulus language learning problem” is far graver than usually supposed; the serial domain where such integration occurs may be the “phase,” which unifies the serial percept with structural assignment and meanings; every level of language processing overlaps with others in a “computational fractal”; each level analysis-by-synthesis interaction of associative-serial and structure dependent processes.
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Book chapters on the topic "Acoustic Unit Discovery"

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Suzuki, Motoyuki, Takafumi Hayashi, Hiroki Mori, Shozo Makino, and Hirotomo Aso. "An Automatic Acquisition of Acoustical Units for Speech Recognition Based on Hidden Markov Network." In Discovery Science, 357–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46846-3_47.

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Pesic, Peter. "Electric Sounds." In Music and the Making of Modern Science. The MIT Press, 2014. http://dx.doi.org/10.7551/mitpress/9780262027274.003.0013.

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Those who followed Leonhard Euler’s wave theory of light often re-engaged its relation to sound. The study of electricity and magnetism resonated with ongoing initiatives in light and sound, reflecting also wider philosophical ideas about the unity of nature epitomized by Naturphilosophie. This chapter examines the intertwined study of electricity and acoustics by Georg Christoph Lichtenberg, Johann Ritter, and Ernst Chladni. The search to unify the forces of nature often relied on analogies with sound, which in turn looked to electricity for new tools. Félix Savart studied the vibration patterns of violins; after reviewing this work, Jean-Baptiste Biot joined Savart in working on electromagnetism. In the aftermath of Thomas Young’s work, waves became a newly attractive explanatory approach to the problems of electricity. Building directly on Chladni’s sound figures, Hans Christian Ørsted discovered the synthesis of “electromagnetism” that brought a new unity to these two formerly separate forces, realizing the unitive hopes of Naturphilosophie. Ørsted’s discovery involved realizing the dynamic, transverse action of electromagnetism, qualities he had previously studied in vibrating plates. Throughout the book where various sound examples are referenced, please see http://mitpress.mit.edu/musicandmodernscience (please note that the sound examples should be viewed in Chrome or Safari Web browsers).
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Marks II, Robert J. "Applications." In Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0018.

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Fourier discovered the Fourier series as a solution to a boundary value problem [33, 303, 512, 620] related to the heat wave equation. Fourier’s work on heat is still in print [455]. In this section, we derive the wave equation for the vibrating string and show how the Fourier series is used in its solution. The solution, in turn, gives rise to the physics of harmonics used as the foundation of music harmony. We contrast the natural harmony of the overtones to that available from the tempered scale of western music. The tempered scale is able to accurately approximate the beauty of natural harmony using a uniformly calibrated frequency scale. The wave equation is manifest in analysis of physical phenomena that display wave like properties. This includes electromagnetic waves, heat waves, and acoustic waves. We consider the case of the simple vibrating string. A string under horizontal tension T is subjected to a small vertical displacement, y = y(x, t), that is a function of time, t, and location, x. As illustrated in Figure 13.1, attention is focused on an incremental piece of the string from x to x +Dx. Under the small displacement assumption, there is no movement of the string horizontally (i.e., in the x direction), and the horizontal forces must sum to zero. T = T1 cos(θ1) = T2 cos(θ2). Let the linear mass density (i.e., mass per unit length) of the string be ρ. The mass of the incremental piece of string is then ρ. The total vertical force acting on the string is T2 cos(θ2) − T1 cos(θ1).
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Conference papers on the topic "Acoustic Unit Discovery"

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Ondel, Lucas, Lukas Burget, Jan Cernocky, and Santosh Kesiraju. "Bayesian phonotactic Language Model for Acoustic Unit Discovery." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953258.

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Liu, Chunxi, Jinyi Yang, Ming Sun, Santosh Kesiraju, Alena Rott, Lucas Ondel, Pegah Ghahremani, Najim Dehak, Lukas Burget, and Sanjeev Khudanpur. "An empirical evaluation of zero resource acoustic unit discovery." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953169.

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Yusuf, Bolaji, Alican Gök, Batuhan Gundogdu, Oyku Deniz Kose, and Murat Saraclar. "Temporally-Aware Acoustic Unit Discovery for Zerospeech 2019 Challenge." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-1430.

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Ondel, Lucas, Hari Krishna Vydana, Lukáš Burget, and Jan Černocký. "Bayesian Subspace Hidden Markov Model for Acoustic Unit Discovery." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2224.

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Ebbers, Janek, Jahn Heymann, Lukas Drude, Thomas Glarner, Reinhold Haeb-Umbach, and Bhiksha Raj. "Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1160.

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Kesiraju, Santosh, Raghavendra Pappagari, Lucas Ondel, Lukas Burget, Najim Dehak, Sanjeev Khudanpur, Jan Cernocky, and Suryakanth V. Gangashetty. "Topic identification of spoken documents using unsupervised acoustic unit discovery." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953257.

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Milde, Benjamin, and Chris Biemann. "SparseSpeech: Unsupervised Acoustic Unit Discovery with Memory-Augmented Sequence Autoencoders." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2938.

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Yusuf, Bolaji, Lucas Ondel, Lukas Burget, Jan Cernocky, and Murat Saraclar. "A Hierarchical Subspace Model for Language-Attuned Acoustic Unit Discovery." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414899.

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Hartmann, William, Anindya Roy, Lori Lamel, and Jean-Luc Gauvain. "Acoustic unit discovery and pronunciation generation from a grapheme-based lexicon." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707760.

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Wang, Rongrong, Lianhai Zhang, and Qi Chen. "Acoustic unit discovery based on multilingual resource using variational Bayesian method." In 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI ). IEEE, 2018. http://dx.doi.org/10.1109/icaci.2018.8377496.

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