Добірка наукової літератури з теми "Spectrogram analysis"
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Статті в журналах з теми "Spectrogram analysis"
Johnson, Alexander. "An integrated approach for teaching speech spectrogram analysis to engineering students." Journal of the Acoustical Society of America 152, no. 3 (September 2022): 1962–69. http://dx.doi.org/10.1121/10.0014172.
Повний текст джерелаLi, Hong Ping, and Hong Li. "Establish an Artificial Neural Networks Model to Make Quantitative Analysis about the Capillary Electrophoresis Spectrum." Advanced Materials Research 452-453 (January 2012): 1116–20. http://dx.doi.org/10.4028/www.scientific.net/amr.452-453.1116.
Повний текст джерелаFerreira, Diogo R., Tiago A. Martins, and Paulo Rodrigues. "Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion." Machine Learning: Science and Technology 3, no. 1 (December 30, 2021): 015015. http://dx.doi.org/10.1088/2632-2153/ac44aa.
Повний текст джерелаLiao, Ying. "Analysis of Rehabilitation Occupational Therapy Techniques Based on Instrumental Music Chinese Tonal Language Spectrogram Analysis." Occupational Therapy International 2022 (October 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/1064441.
Повний текст джерелаPethiyagoda, Ravindra, Scott W. McCue, and Timothy J. Moroney. "Spectrograms of ship wakes: identifying linear and nonlinear wave signals." Journal of Fluid Mechanics 811 (December 6, 2016): 189–209. http://dx.doi.org/10.1017/jfm.2016.753.
Повний текст джерелаOh, Myeonggeun, and Yong-Hoon Kim. "Statistical Approach to Spectrogram Analysis for Radio-Frequency Interference Detection and Mitigation in an L-Band Microwave Radiometer." Sensors 19, no. 2 (January 14, 2019): 306. http://dx.doi.org/10.3390/s19020306.
Повний текст джерелаPizetta, Igor Henrique Beloti, Alexandre Santos Brandão, and Mário Sarcinelli-Filho. "UAV Thrust Model Identification Using Spectrogram Analysis." Automation 2, no. 3 (August 1, 2021): 141–52. http://dx.doi.org/10.3390/automation2030009.
Повний текст джерелаFranzoni, Valentina, Giulio Biondi, and Alfredo Milani. "Emotional sounds of crowds: spectrogram-based analysis using deep learning." Multimedia Tools and Applications 79, no. 47-48 (August 17, 2020): 36063–75. http://dx.doi.org/10.1007/s11042-020-09428-x.
Повний текст джерелаSun, Hong-ying, Hong-wu Yang, and Zhong-xing Tao. "Realization of Spectrogram Analysis with FPGA." Journal of Electronics & Information Technology 33, no. 5 (May 12, 2011): 1238–42. http://dx.doi.org/10.3724/sp.j.1146.2010.01005.
Повний текст джерелаELEMANS, COEN P. H., KIER HEECK, and MEES MULLER. "SPECTROGRAM ANALYSIS OF ANIMAL SOUND PRODUCTION." Bioacoustics 18, no. 2 (January 2008): 183–212. http://dx.doi.org/10.1080/09524622.2008.9753599.
Повний текст джерелаДисертації з теми "Spectrogram analysis"
Bleakley, Steven Shea, and steven bleakley@qr com au. "Time Frequency Analysis of Railway Wagon Body Accelerations for a Low-Power Autonomous Device." Central Queensland University, 2006. http://library-resources.cqu.edu.au./thesis/adt-QCQU/public/adt-QCQU20070622.121515.
Повний текст джерелаKalm, Helen. "Acoustic Soil-Rock Probing : A Case Study in Gubbängen." Thesis, KTH, Jord- och bergmekanik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-256081.
Повний текст джерелаYao, Bing. "ANALYSIS OF ELECTRICAL AND MAGNETIC BIO-SIGNALS ASSOCIATED WITH MOTOR PERFORMANCE AND FATIGUE." Case Western Reserve University School of Graduate Studies / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=case1140813534.
Повний текст джерелаKrejčí, Michal. "Fourierova transformace a spektrogramy v analýze DNA sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2011. http://www.nusl.cz/ntk/nusl-219249.
Повний текст джерелаDAK, HAZIRBABA YILDIZ. "IMAGE-BASED MODELING AND PREDICTION OF NON-STATIONARY GROUND MOTIONS." OpenSIUC, 2015. https://opensiuc.lib.siu.edu/dissertations/1008.
Повний текст джерелаKubánková, Anna. "Automatická klasifikace digitálních modulací." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233424.
Повний текст джерелаOliveira, Marlice Fernandes de. "Estudo da separação entre voz patológica e normal por meio da avaliação da energia global do sinal de voz." Universidade Federal de Uberlândia, 2007. https://repositorio.ufu.br/handle/123456789/14661.
Повний текст джерелаA análise do sinal de voz é uma ferramenta importante no diagnóstico dos distúrbios laríngeos. Dentre as diversas técnicas para o processamento da voz destaca-se o espectrograma por permitir uma visualização da variação da energia do sinal em função do tempo e freqüência. Neste contexto, esta pesquisa investiga a energia global do sinal de voz, estimada a partir do espectrograma, como ferramenta capaz de discriminar esses sinais, obtidos de pacientes com diferentes doenças, daqueles coletados de sujeitos saudáveis. O estudo ainda verifica a possibilidade do uso da energia global na discriminação de distúrbios laríngeos. No total 94 indivíduos participaram desse estudo, sendo 46 disfônicos e 48 eufônicos. Inicialmente os sujeitos foram submetidos a um exame de videolaringoscopia para a determinaçãoção do diagnóstico. Posteriormente eles foram submetidos a um exame clínico de análise acústica vocal por meio da gravação da vogal sustentada. Utilizando a energia global foi possível discriminar as vozes normais das vozes disfônicas. Por meio da energia global foi possível ainda separar as vozes de pacientes portadores de paralisia da prega vocal esquerda de todas as outras doenças laríngeas investigadas. Podemos sugerir a energia global da voz como uma ferramenta auxiliar no diagnóstico diferencial entre vozes normais e disfônicas.
Mestre em Ciências
Ford, George Harold. "Spoken Language Identification from Processing and Pattern Analysis of Spectrograms." NSUWorks, 2014. http://nsuworks.nova.edu/gscis_etd/152.
Повний текст джерелаTAECHAWATTANANANT, PASRAWIN. "Peak identification and quantification in proteomic mass spectrograms using non-negative matrix factorization." Doctoral thesis, Kyoto University, 2020. http://hdl.handle.net/2433/253496.
Повний текст джерела0048
新制・課程博士
博士(薬科学)
甲第22651号
薬科博第123号
新制||薬科||13(附属図書館)
京都大学大学院薬学研究科薬科学専攻
(主査)教授 石濱 泰, 教授 緒方 博之, 教授 馬見塚 拓, 教授 山下 富義
学位規則第4条第1項該当
Doctor of Pharmaceutical Sciences
Kyoto University
DFAM
Movin, Andreas, and Jonathan Jilg. "Kan datorer höra fåglar?" Thesis, KTH, Skolan för teknikvetenskap (SCI), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254800.
Повний текст джерелаSound recognition is made possible through spectral analysis, computed by the fast Fourier transform (FFT), and has in recent years made major breakthroughs along with the rise of computational power and artificial intelligence. The technology is now used ubiquitously and in particular in the field of bioacoustics for identification of animal species, an important task for wildlife monitoring. It is still a growing field of science and especially the recognition of bird song which remains a hard-solved challenge. Even state-of-the-art algorithms are far from error-free. In this thesis, simple algorithms to match sounds to a sound database were implemented and assessed. A filtering method was developed to pick out characteristic frequencies at five time frames which were the basis for comparison and the matching procedure. The sounds used were pre-recorded bird songs (blackbird, nightingale, crow and seagull) as well as human voices (4 young Swedish males) that we recorded. Our findings show success rates typically at 50–70%, the lowest being the seagull of 30% for a small database and the highest being the blackbird at 90% for a large database. The voices were more difficult for the algorithms to distinguish, but they still had an overall success rate between 50% and 80%. Furthermore, increasing the database size did not improve success rates in general. In conclusion, this thesis shows the proof of concept and illustrates both the strengths as well as short-comings of the simple algorithms developed. The algorithms gave better success rates than pure chance of 25% but there is room for improvement since the algorithms were easily misled by sounds of the same frequencies. Further research will be needed to assess the devised algorithms' ability to identify even more birds and voices.
Книги з теми "Spectrogram analysis"
Astronomical spectrographs and their history. Cambridge: Cambridge University Press, 2009.
Знайти повний текст джерелаMcDermott, A. J. Development of a spectrograph for the analysis of speech. Manchester: UMIST, 1995.
Знайти повний текст джерелаAdelman, Saul J. Photographic region elemental abundance analyses of Dr. David S. Leckrone's GTO HST stars II: Final report. [Washington, DC: National Aeronautics and Space Administration, 1994.
Знайти повний текст джерелаHymas, Hewitt M. A calibration of the Naval Postgraduate School middle ultraviolet spectrograph and an analysis of the OII 2470 Å emission obtained by the middle ultraviolet spectrograph. Monterey, Calif: Naval Postgraduate School, 1994.
Знайти повний текст джерелаMarron, Antony C. An analysis of ionospheric dayglow from observations of the Naval Postgraduate School Middle Ultraviolet Spectrograph (MUSTANG). Monterey, Calif: Naval Postgraduate School, 1993.
Знайти повний текст джерелаa, M. Time Frequency Analysis: Comparisons Between WVD and Spectrogram with Application. Independently Published, 2019.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Photographic region elemental abundance analyses of Dr. David S. Leckrone's GTO HST stars II: Final report. [Washington, DC: National Aeronautics and Space Administration, 1994.
Знайти повний текст джерелаЧастини книг з теми "Spectrogram analysis"
Lorenzo, Javier, and Mario Hernández. "Habituation Based on Spectrogram Analysis." In Advances in Artificial Intelligence — IBERAMIA 2002, 893–902. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_91.
Повний текст джерелаMontalvo, Ana, Yandre M. G. Costa, and José Ramón Calvo. "Language Identification Using Spectrogram Texture." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 543–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25751-8_65.
Повний текст джерелаChassande-Mottin, Eric, Patrick Flandrin, and François Auger. "On the Statistics of Spectrogram Reassignment Vectors." In Recent Developments in Time-Frequency Analysis, 23–30. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2838-5_3.
Повний текст джерелаLe Roux, Jonathan, Emmanuel Vincent, Yuu Mizuno, Hirokazu Kameoka, Nobutaka Ono, and Shigeki Sagayama. "Consistent Wiener Filtering: Generalized Time-Frequency Masking Respecting Spectrogram Consistency." In Latent Variable Analysis and Signal Separation, 89–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15995-4_12.
Повний текст джерелаDrgas, Szymon, and Tuomas Virtanen. "Speaker Verification Using Adaptive Dictionaries in Non-negative Spectrogram Deconvolution." In Latent Variable Analysis and Signal Separation, 462–69. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22482-4_54.
Повний текст джерелаDuc, Trinh Quang, Nguyen Van Son, Nguyen Hoai Giang, Dao Huy Du, and Ha Ngoc Thu. "An Analysis of Lung Sound from Electronic Stethoscope with Spectrogram." In Advances in Engineering Research and Application, 107–13. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37497-6_12.
Повний текст джерелаBodusz, Wojciech, Zuzanna Miodońska, and Paweł Badura. "Approach for spectrogram analysis in detection of selected pronunciation pathologies." In Innovations in Biomedical Engineering, 3–11. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70063-2_1.
Повний текст джерелаOswald, Julie N., Christine Erbe, William L. Gannon, Shyam Madhusudhana, and Jeanette A. Thomas. "Detection and Classification Methods for Animal Sounds." In Exploring Animal Behavior Through Sound: Volume 1, 269–317. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97540-1_8.
Повний текст джерелаYuan, Longhao, and Jianting Cao. "Patients’ EEG Data Analysis via Spectrogram Image with a Convolution Neural Network." In Intelligent Decision Technologies 2017, 13–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59421-7_2.
Повний текст джерелаSouli, Sameh, Zied Lachiri, and Alexander Kuznietsov. "Using Three Reassigned Spectrogram Patches and Log-Gabor Filter for Audio Surveillance Application." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 527–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41822-8_66.
Повний текст джерелаТези доповідей конференцій з теми "Spectrogram analysis"
Zawawi, T. N. S. T., A. R. Abdullah, E. F. Shair, I. Halim, and O. Rawaida. "Electromyography signal analysis using spectrogram." In 2013 IEEE Student Conference on Research and Development (SCOReD). IEEE, 2013. http://dx.doi.org/10.1109/scored.2013.7002599.
Повний текст джерелаDoura, Tomoki, and Toshihiko Shiraishi. "Sound Source Separation Using Spectrogram Analysis by Neural Networks." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71583.
Повний текст джерелаXu, Qiang. "Jet noise analysis by Gabor spectrogram." In Defense and Security Symposium, edited by Harold H. Szu. SPIE, 2006. http://dx.doi.org/10.1117/12.655467.
Повний текст джерелаBenedetto, John J., and David Colella. "Wavelet analysis of spectrogram seizure chirps." In SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, edited by Andrew F. Laine and Michael A. Unser. SPIE, 1995. http://dx.doi.org/10.1117/12.217606.
Повний текст джерелаPatzold, Matthias, and Neji Youssef. "Spectrogram analysis of multipath fading channels." In 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC). IEEE, 2015. http://dx.doi.org/10.1109/pimrc.2015.7343665.
Повний текст джерелаGarcia-Casado, J., J. L. Martinez-de-Juan, G. Prats-Boluda, J. Bertelli, and J. L. Ponce. "Adaptive Spectrogram for Surface EEnG Analysis." In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1616546.
Повний текст джерелаDugnol, B., C. Fernández, G. Galiano, and J. Velasco. "Evolution Nonlinear Diffusion‐Convection PDE Models for Spectrogram Enhancement." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990882.
Повний текст джерелаLi, Jiarui, and Ying Hong. "Wheeze Detection Algorithm Based on Spectrogram Analysis." In 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.310.
Повний текст джерелаLatif, R., E. Aassif, M. Laaboubi, and G. Maze. "Dimensional characterization of an elastic tube using the spectrogram and the reassigned spectrogram time-frequency analysis." In 2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP). IEEE, 2010. http://dx.doi.org/10.1109/isccsp.2010.5463487.
Повний текст джерелаChu, Jianjun, Yuejuan Jiang, Quanchen Li, and Dazun Zhao. "Interferogram processing with wavelet analysis and spectrogram reconstruction." In Optics and Optoelectronic Inspection and Control: Techniques, Applications, and Instruments, edited by Shulian Zhang and Wei Gao. SPIE, 2000. http://dx.doi.org/10.1117/12.403916.
Повний текст джерелаЗвіти організацій з теми "Spectrogram analysis"
Sylvia, J. M., J. W. Haas, K. M. Spencer, M. M. Carrabba, R. D. Rauh, R. W. Forney, and T. M. Johnston. Field Raman Spectrograph for Environmental Analysis. Office of Scientific and Technical Information (OSTI), July 1998. http://dx.doi.org/10.2172/3969.
Повний текст джерела