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Title:Klasifikacija glasbenega žanra glede na spektrogram zvočnega posnetka : diplomsko delo
Authors:ID Lahovnik, Tadej (Author)
ID Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UN_Lahovnik_Tadej_2022.pdf (1,50 MB)
MD5: FE99FAE33E251784390637369AD765EC
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomskem delu smo se poglobili v izdelavo različnih tipov spektrogramov in klasifikacijo slik z uporabo konvolucijskih nevronskih mrež. Zanimalo nas je, ali je možno zanesljivo napovedati žanr zvočnega posnetka glede na spektrogram, ki mu pripada. Tekom razvoja smo ustvarili tri različne tipe spektrogramov. Za vsak tip smo ustvarili ločen klasifikacijski model, nato pa smo iz vseh treh modelov sestavili klasifikacijski ansambel. Tako smo dobili najbolj zanesljive rezultate. Klasifikacijo smo nato ovrednotili s številnimi metrikami, kjer nas je najbolj zanimala sama točnost klasifikacije. Iz matrike zmede smo izčrpali najpogostejše napake pri klasifikaciji.
Keywords:klasifikacija, spektrogram, strojno učenje, nevronske mreže, glasbeni žanr
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[T. Lahovnik]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (X, 36 f.))
PID:20.500.12556/DKUM-82370 New window
UDC:004.85:004.932(043.2)
COBISS.SI-ID:130638083 New window
Publication date in DKUM:20.10.2022
Views:3264
Downloads:77
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:17.08.2022

Secondary language

Language:English
Title:Music genre classification based on the spectrogram of the sound recording
Abstract:In our thesis, we delved into the generation of diverse types of spectrograms and image classification using convolutional neural networks. We were interested in whether it is possible to reliably predict the genre of an audio recording based on its spectrogram. During our development, we created three distinct types of spectrograms. We created a separate classifier model for each type and then built a classifier ensemble from all three models. In this way, we obtained the most reliable results. We then evaluated the classification with several metrics, where we were most interested in the accuracy of the classification. We extracted the most common classification errors from the confusion matrix.
Keywords:classification, spectrogram, machine learning, neural networks, music genre


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