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Letteratura scientifica selezionata sul tema "Audio data mining"

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Tesi sul tema "Audio data mining"

1

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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2

Kohlsdorf, Daniel. "Data mining in large audio collections of dolphin signals." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53968.

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Abstract (sommario):
The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics bet
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Thambiratnam, 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.

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Abstract (sommario):
Keyword Spotting is the task of detecting keywords of interest within continu- ous speech. The applications of this technology range from call centre dialogue systems to covert speech surveillance devices. Keyword spotting is particularly well suited to data mining tasks such as real-time keyword monitoring and unre- stricted vocabulary audio document indexing. However, to date, many keyword spotting approaches have su®ered from poor detection rates, high false alarm rates, or slow execution times, thus reducing their commercial viability. This work investigates the application of keyword spot
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4

Fenet, 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.

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Abstract (sommario):
Dans cet ouvrage, nous définissons précisément ce qu’est l’identification audio à grande échelle. En particulier, nous faisons une distinction entre l’identification exacte, destinée à rapprocher deux extraits sonores provenant d’un même enregistrement, et l’identification approchée, qui gère également la similarité musicale entre les signaux. A la lumière de ces définitions, nous concevons et examinons plusieurs modèles d’empreinte audio et évaluons leurs performances, tant en identification exacte qu’en identificationapprochée<br>N this work we give a precise definition of large scale audio
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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.

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Abstract (sommario):
Dans cet ouvrage, nous définissons précisément ce qu’est l’identification audio à grande échelle. En particulier, nous faisons une distinction entre l’identification exacte, destinée à rapprocher deux extraits sonores provenant d’un même enregistrement, et l’identification approchée, qui gère également la similarité musicale entre les signaux. A la lumière de ces définitions, nous concevons et examinons plusieurs modèles d’empreinte audio et évaluons leurs performances, tant en identification exacte qu’en identificationapprochée<br>N this work we give a precise definition of large scale audio
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6

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.

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This thesis is concerned with the detection and prediction of rain in environmental recordings using different machine learning algorithms. The results obtained in this research will help ecologists to efficiently analyse environmental data and monitor biodiversity.
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7

Bayle, Yann. "Apprentissage automatique de caractéristiques audio : application à la génération de listes de lecture thématiques." Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0087/document.

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Ce mémoire de thèse de doctorat présente, discute et propose des outils de fouille automatique de mégadonnées dans un contexte de classification supervisée musical.L'application principale concerne la classification automatique des thèmes musicaux afin de générer des listes de lecture thématiques.Le premier chapitre introduit les différents contextes et concepts autour des mégadonnées musicales et de leur consommation.Le deuxième chapitre s'attelle à la description des bases de données musicales existantes dans le cadre d'expériences académiques d'analyse audio.Ce chapitre introduit notamment
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8

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.

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Abstract (sommario):
For the first time in human history, large volumes of spoken audio are being broadcast, made available on the internet, archived, and monitored for surveillance every day. New technologies are urgently required to unlock these vast and powerful stores of information. Spoken Term Detection (STD) systems provide access to speech collections by detecting individual occurrences of specified search terms. The aim of this work is to develop improved STD solutions based on phonetic indexing. In particular, this work aims to develop phonetic STD systems for applications that require open-vocabulary se
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9

Ziegler, Thomas. "Auswertung von Audit-Daten zur Optimierung von Workflows." [S.l. : s.n.], 2001. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB9386075.

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10

Wang, 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.

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