Prepoznavanje obilježja glazbe primjenom strojnog učenja

Malkoč, Marko

prikaz prve stranice dokumenta Prepoznavanje obilježja glazbe primjenom strojnog učenja

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master's thesis

Prepoznavanje obilježja glazbe primjenom strojnog učenja

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:341915

Malkoč, Marko

University of Zagreb
Faculty of Electrical Engineering and Computing

Cite this document

APA 6th Edition

Malkoč, M. (2023). Prepoznavanje obilježja glazbe primjenom strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:341915

MLA 8th Edition

Malkoč, Marko. "Prepoznavanje obilježja glazbe primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:341915

Chicago 17th Edition

Malkoč, Marko. "Prepoznavanje obilježja glazbe primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:341915

Harvard

Malkoč, M. (2023). 'Prepoznavanje obilježja glazbe primjenom strojnog učenja', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 12 January 2025, https://urn.nsk.hr/urn:nbn:hr:168:341915

Vancouver

Malkoč M. Prepoznavanje obilježja glazbe primjenom strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2025 January 12] Available at: https://urn.nsk.hr/urn:nbn:hr:168:341915

IEEE

M. Malkoč, "Prepoznavanje obilježja glazbe primjenom strojnog učenja", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:341915

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:341915

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Metadata

Title	Prepoznavanje obilježja glazbe primjenom strojnog učenja
Title (english)	Recognizing music characteristics using machine learning
Author	Marko Malkoč
Mentor	Marko Jurčević (mentor)
Committee member	Marko Jurčević (predsjednik povjerenstva)
Committee member	Ivan Leniček (član povjerenstva)
Committee member	Jure Konjevod (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing Zagreb
Defense date and country	2023-09-21, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Abstract	Rad se bavi primjenom strojnog učenja na problem procjene emocionalnog doživljaja glazbe. Razmatrane su različite psihološke teorije emocija te psihološka perspektiva odnosa glazbe i emocija. U sklopu rada prikupljen je skup podataka koji glazbenim isječcima dodjeljuje ocjenu emocije. Na tom skupu podatka treniran je model dubokog učenja koji nije uspio postići željene rezultate, vjerojatno zbog male veličine skupa podataka. Razmotreni su različiti načini obrade digitalnih zvučnih signala i na što je potrebno obratiti pozornost prilikom pripreme zvučnih podatka za strojno učenje. Implementirana je aplikacija za korištenje modela koja reproducira zvučnu datoteku, vizualizirajući zvučni signal te pomoću modela procjenjuje prenesenu emociju reproducirane glazbe.
Abstract (english)	The paper deals with the application of machine learning to the problem of assessing the emotional experience of music. Various psychological theories of emotion were examined and the psychological perspective of the relationship between music and emotions was given a closer look. As part of the work, a dataset was collected that assigns an emotion rating to music clips. A deep learning model was trained on that dataset and failed to achieve the desired results, probably due to the small size of the dataset. Different ways of processing digital sound signals are discussed and what needs to be paid attention to when preparing sound data for machine learning. An application has been implemented for using the model. It reproduces a sound file, visualizing the sound signal and uses the model to evaluate the conveyed emotion of the reproduced music.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:341915
Study programme	Title: Computing Study programme type: university Study level: graduate Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resource	Text
File origin	Born digital
Access conditions	Closed access
Terms of use
Public note
Repository	FER Repository
Created on	2023-10-11 09:38:28