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master's thesis
Primjena dubokog učenja u obradi zvuka
Zagreb: University of Zagreb, Faculty of Science, 2018. urn:nbn:hr:217:474439
Vitez, Petra
University of Zagreb
Faculty of Science
Department of Mathematics

Institutional repository: Repository of the Faculty of Science

Cite this document

Vitez, P. (2018). Primjena dubokog učenja u obradi zvuka (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:474439

Vitez, Petra. "Primjena dubokog učenja u obradi zvuka." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:474439

Vitez, Petra. "Primjena dubokog učenja u obradi zvuka." Master's thesis, University of Zagreb, Faculty of Science, 2018. https://urn.nsk.hr/urn:nbn:hr:217:474439

Vitez, P. (2018). 'Primjena dubokog učenja u obradi zvuka', Master's thesis, University of Zagreb, Faculty of Science, accessed 23 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:474439

Vitez P. Primjena dubokog učenja u obradi zvuka [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2018 [cited 2024 December 23] Available at: https://urn.nsk.hr/urn:nbn:hr:217:474439

P. Vitez, "Primjena dubokog učenja u obradi zvuka", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2018. Available at: https://urn.nsk.hr/urn:nbn:hr:217:474439

Metadata
TitlePrimjena dubokog učenja u obradi zvuka
AuthorPetra Vitez
MentorSaša Singer (mentor)
Committee memberSaša Singer (predsjednik povjerenstva)
Committee memberRobert Manger (član povjerenstva)
Committee memberIlja Gogić (član povjerenstva)
Committee memberGoran Muić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Mathematics)
Zagreb
Defense date and country2018-09-26, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Mathematics
Abstract
U ovom radu objašnjavamo pojam neuronskih mreža i promatramo njihov matematički aspekt. Dajemo generalni uvid u arhitekturu neuronskih mreža, te način na koji neuronska mreža uči i kako se zatim ponaša na dosad neviđenim podacima. Zatim, dajemo naglasak na rekurentne neuronske mreže i kako one rješavaju problem klasifikacije nizova. Objašnjavamo posebnu arhitekturu rekurentnih neuronskih mreža, tzv. Long-Short Term Memory arhitekturu. Na kraju, pokazujemo kako smo iskoristili programski jezik Python i biblioteku otvorenog koda Tensorflow, za izradu modela rekurentne neuronske mreže, koji se sastoji od dvije Long-Short Term Memory jedinice, kako bismo riješili problem klasifikacije gradskih zvukova, na UrbanSound 8k skupu podataka. Koristimo metodu unakrsne validacije i, kao rezultate, prikazujemo prosječnu točnost koju dobivamo na testnim skupovima podataka, te matricu konfuzije za jedan testni skup podataka.
Abstract (english)
In this thesis we explain the concept of neural networks and observe their mathematical aspect. We give a general insight into the architecture of neural networks, the way the neural network learns, and how it behaves on previosly unseen data. Then, we give a special accent to recurrent neural networks and how they solve the problem of sequence classification. We explain a special architecture of recurrent neural networks, the so-called Long-Short Term Memory architecture. In the end, we present how we used Python programming language and the open source library Tensorflow, to create a recurrent neuronal network model that consists of two LongShort Term Memory units, to solve the problem of urban sound classification, on the Urban Sound 8k data set. We use the cross-validation method and, as results, we give the average accuracy obtained for test datasets, and the confusion matrix for one test dataset.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:474439
Study programmeTitle: Computer Science and Mathematics
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra računarstva i matematike (magistar/magistra računarstva i matematike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2019-07-03 12:57:48