Prepoznavanje obilježja glazbe primjenom strojnog učenja

master's thesis

• 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: strojno učenje; obrada zvuka; psihologija emocija; glazba; prikupljanje podataka
• Keywords (english): machine learning; sound data processing; psychology of emotions; music; data collection
• 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
• Created on: 2023-10-11 09:38:28