Letteratura scientifica selezionata sul tema "Audio data mining"
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Articoli di riviste sul tema "Audio data mining"
Xu, Shasha. "Effective Graph Mining for Educational Data Mining and Interest Recommendation". Applied Bionics and Biomechanics 2022 (12 agosto 2022): 1–5. http://dx.doi.org/10.1155/2022/7610124.
Testo completoXu, Yanping, e Sen Xu. "A Clustering Analysis Method for Massive Music Data". Modern Electronic Technology 5, n. 1 (6 maggio 2021): 24. http://dx.doi.org/10.26549/met.v5i1.6763.
Testo completoTHURAISINGHAM, BHAVANI. "MANAGING AND MINING MULTIMEDIA DATABASES". International Journal on Artificial Intelligence Tools 13, n. 03 (settembre 2004): 739–59. http://dx.doi.org/10.1142/s0218213004001776.
Testo completoWang, Fang. "The Effect of Multimedia Teaching Model of Music Course in Colleges and Universities Based on Classroom Audio Data Mining Technology". Tobacco Regulatory Science 7, n. 5 (30 settembre 2021): 4520–31. http://dx.doi.org/10.18001/trs.7.5.2.18.
Testo completoPaul, Prantosh K., e K. S. Shivraj. "Multimedia Data Mining and its Integration in Information Sector and Foundation: An Overview". Asian Journal of Computer Science and Technology 3, n. 1 (5 maggio 2014): 24–28. http://dx.doi.org/10.51983/ajcst-2014.3.1.1729.
Testo completoYe, Jiaxing, Takumi Kobayashi, Xiaoyan Wang, Hiroshi Tsuda e Masahiro Murakawa. "Audio Data Mining for Anthropogenic Disaster Identification: An Automatic Taxonomy Approach". IEEE Transactions on Emerging Topics in Computing 8, n. 1 (1 gennaio 2020): 126–36. http://dx.doi.org/10.1109/tetc.2017.2700843.
Testo completoLi, Xaiomeng. "Construction of Teachers Performance Evaluation Index System for Data-Driven Smart Classrooms in Secondary Schools". SHS Web of Conferences 190 (2024): 03010. http://dx.doi.org/10.1051/shsconf/202419003010.
Testo completoShin, Sanghyun, Abhishek Vaidya e Inseok Hwang. "Helicopter Cockpit Audio Data Analysis to Infer Flight State Information". Journal of the American Helicopter Society 65, n. 3 (1 luglio 2020): 1–8. http://dx.doi.org/10.4050/jahs.65.032001.
Testo completoFaridzi, Salman Al, Faza Shafa Azizah, Faizal Mustafa, Azzahra Nindya Putri, Gilang Ramadhika, Fauzan Rizky Aditya, Ridha Sherli Fadilah et al. "PENGOLAHAN DATA: PEMAHAMAN GEMPA BUMI DI INDONESIA MELALUI PENDEKATAN DATA MINING". Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS 2, n. 1 (16 febbraio 2024): 262–70. http://dx.doi.org/10.59407/jpki2.v2i1.506.
Testo completoBhoyar, Sanjay, Punam Bhoyar, Anuj Kumar e Prabha Kiran. "Enhancing applications of surveillance through multimedia data mining". Journal of Discrete Mathematical Sciences and Cryptography 27, n. 3 (2024): 1105–20. http://dx.doi.org/10.47974/jdmsc-1947.
Testo completoTesi sul tema "Audio data mining"
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.
Testo completoKohlsdorf, Daniel. "Data mining in large audio collections of dolphin signals". Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53968.
Testo completoThambiratnam, Albert J. K. "Acoustic keyword spotting in speech with applications to data mining". Thesis, Queensland University of Technology, 2005. https://eprints.qut.edu.au/37254/1/Albert_Thambiratnam_Thesis.pdf.
Testo completoFenet, Sébastien. "Empreintes audio et stratégies d'indexation associées pour l'identification audio à grande échelle". Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0051/document.
Testo completoN this work we give a precise definition of large scale audio identification. In particular, we make a distinction between exact and approximate matching. In the first case, the goal is to match two signals coming from one same recording with different post-processings. In the second case, the goal is to match two signals that are musically similar. In light of these definitions, we conceive and evaluate different audio-fingerprint models
Fenet, Sébastien. "Empreintes audio et stratégies d'indexation associées pour l'identification audio à grande échelle". Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0051.
Testo completoN this work we give a precise definition of large scale audio identification. In particular, we make a distinction between exact and approximate matching. In the first case, the goal is to match two signals coming from one same recording with different post-processings. In the second case, the goal is to match two signals that are musically similar. In light of these definitions, we conceive and evaluate different audio-fingerprint models
Ferroudj, Meriem. "Detection of rain in acoustic recordings of the environment using machine learning techniques". Thesis, Queensland University of Technology, 2015. https://eprints.qut.edu.au/82848/1/Meriem_Ferroudj_Thesis.pdf.
Testo completoBayle, Yann. "Apprentissage automatique de caractéristiques audio : application à la génération de listes de lecture thématiques". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0087/document.
Testo completoThis doctoral dissertation presents, discusses and proposes tools for the automatic information retrieval in big musical databases.The main application is the supervised classification of musical themes to generate thematic playlists.The first chapter introduces the different contexts and concepts around big musical databases and their consumption.The second chapter focuses on the description of existing music databases as part of academic experiments in audio analysis.This chapter notably introduces issues concerning the variety and unequal proportions of the themes contained in a database, which remain complex to take into account in supervised classification.The third chapter explains the importance of extracting and developing relevant audio features in order to better describe the content of music tracks in these databases.This chapter explains several psychoacoustic phenomena and uses sound signal processing techniques to compute audio features.New methods of aggregating local audio features are proposed to improve song classification.The fourth chapter describes the use of the extracted audio features in order to sort the songs by themes and thus to allow the musical recommendations and the automatic generation of homogeneous thematic playlists.This part involves the use of machine learning algorithms to perform music classification tasks.The contributions of this dissertation are summarized in the fifth chapter which also proposes research perspectives in machine learning and extraction of multi-scale audio features
Wallace, Roy Geoffrey. "Fast and accurate phonetic spoken term detection". Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/39610/1/Roy_Wallace_Thesis.pdf.
Testo completoZiegler, Thomas. "Auswertung von Audit-Daten zur Optimierung von Workflows". [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9386075.
Testo completoWang, Tian. "Effective Thermal Resistance of Commercial Buildings Using Data Analysis of Whole-Building Electricity Data". Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586524438396894.
Testo completoLibri sul tema "Audio data mining"
Baronas, Roberto, e Maria Inês Pagliarini Cox. Linguística popular: Folk linguistics : práticas, proposições e polêmicas - homenagem a Amadeu Amaral. Campinas, SP: Pontes, 2020.
Cerca il testo completoMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Cerca il testo completoMultimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.
Cerca il testo completoMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Cerca il testo completoMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Cerca il testo completoMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. Wiley & Sons, Limited, John, 2012.
Cerca il testo completoFinancial management: Fiscal year 1992 audit of the Defense Cooperation Account : report to the Congress. Washington, D.C: The Office, 1993.
Cerca il testo completoCapitoli di libri sul tema "Audio data mining"
Sai Tharun, A., K. Dhivakar e R. Nair Prashant. "Voice Data-Mining on Audio from Audio and Video Clips". In Smart Innovation, Systems and Technologies, 519–34. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-7447-2_46.
Testo completoZheng, Meizhen, Peng Bai e Xiaodong Shi. "A Compact Phoneme-To-Audio Aligner for Singing Voice". In Advanced Data Mining and Applications, 183–97. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_13.
Testo completoNguyen, Cong Phuong, Ngoc Yen Pham e Eric Castelli. "First Steps to an Audio Ontology-Based Classifier for Telemedicine". In Advanced Data Mining and Applications, 845–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_92.
Testo completoLiu, Qingzhong, Andrew H. Sung e Mengyu Qiao. "Spectrum Steganalysis of WAV Audio Streams". In Machine Learning and Data Mining in Pattern Recognition, 582–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03070-3_44.
Testo completoBansal, Mohit, Satya Jeet Raj Upali e Sukesha Sharma. "Early Parkinson Disease Detection Using Audio Signal Processing". In Emerging Technologies in Data Mining and Information Security, 243–50. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4193-1_23.
Testo completoKubera, Elżbieta, e Alicja A. Wieczorkowska. "Mining Audio Data for Multiple Instrument Recognition in Classical Music". In New Frontiers in Mining Complex Patterns, 246–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08407-7_16.
Testo completoGao, Jie, Yanqing Sun, Hongbin Suo, Qingwei Zhao e Yonghong Yan. "WAPS: An Audio Program Surveillance System for Large Scale Web Data Stream". In Web Information Systems and Mining, 116–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-05250-7_13.
Testo completoPark, Dong-Chul, Yunsik Lee e Dong-Min Woo. "Classification of Audio Signals Using a Bhattacharyya Kernel-Based Centroid Neural Network". In Advances in Knowledge Discovery and Data Mining, 604–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01307-2_59.
Testo completoDas, Sanghamitra, Suchibrota Dutta, Debanjan Banerjee e Arijit Ghosal. "Classification of Bharatnatyam and Kathak Dance Form Through Audio Signal". In Emerging Technologies in Data Mining and Information Security, 671–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9774-9_62.
Testo completoMuhammad, Atta, e Sher Muhammad Daudpota. "Content Based Identification of Talk Show Videos Using Audio Visual Features". In Machine Learning and Data Mining in Pattern Recognition, 267–83. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41920-6_20.
Testo completoAtti di convegni sul tema "Audio data mining"
Tao, Yudong, Samantha G. Mitsven, Lynn K. Perry, Daniel S. Messinger e Mei-Ling Shyu. "Audio-Based Group Detection for Classroom Dynamics Analysis". In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00125.
Testo completo"Improvement of Indexing Methods for Audio Fingerprinting Systems". In International Conference Data Mining, Civil and Mechanical Engineering. International Institute of Engineers, 2014. http://dx.doi.org/10.15242/iie.e0214053.
Testo completoRodrigues, João Pedro, e Emerson Paraiso. "From audio to information: Learning topics from audio transcripts". In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/kdmile.2020.11967.
Testo completoHao, Yuan, Mohammad Shokoohi-Yekta, George Papageorgiou e Eamonn Keogh. "Parameter-Free Audio Motif Discovery in Large Data Archives". In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.30.
Testo completoShen, Jiaxing, Oren Lederman, Jiannong Cao, Florian Berg, Shaojie Tang e Alex Pentland. "GINA: Group Gender Identification Using Privacy-Sensitive Audio Data". In 2018 IEEE International Conference on Data Mining (ICDM). IEEE, 2018. http://dx.doi.org/10.1109/icdm.2018.00061.
Testo completoS. N. Lagmiri e H. Bakhous. "AUDIO ENCRYPTION ALGORITHM USING HYPERCHAOTIC SYSTEMS OF DIFFERENT DIMENSIONS". In 3rd International Conference on Data Mining & Knowledge Management. AIRCC Publication Corporation, 2018. http://dx.doi.org/10.5121/csit.2018.81507.
Testo completoEbrahimi, Samaneh, Hossein Vahabi, Matthew Prockup e Oriol Nieto. "Predicting Audio Advertisement Quality". In WSDM 2018: The Eleventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3159652.3159701.
Testo completoYu, Lingyun, Jun Yu e Qiang Ling. "Mining Audio, Text and Visual Information for Talking Face Generation". In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00089.
Testo completoSageder, Gerhard, Maia Zaharieva e Matthias Zeppelzauer. "Unsupervised Selection of Robust Audio Feature Subsets". In Proceedings of the 2014 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.79.
Testo completoChu, Eric, e Deb Roy. "Audio-Visual Sentiment Analysis for Learning Emotional Arcs in Movies". In 2017 IEEE International Conference on Data Mining (ICDM). IEEE, 2017. http://dx.doi.org/10.1109/icdm.2017.100.
Testo completoRapporti di organizzazioni sul tema "Audio data mining"
Craven, J. A., G. McNeice, B. Powell, R. Koch, I R Annesley, G. Wood e 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.
Testo completoJajodia, Sushi. Integration of Audit Data Analysis and Mining Techniques into Aide. Fort Belvoir, VA: Defense Technical Information Center, luglio 2006. http://dx.doi.org/10.21236/ada456840.
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