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Title:Klasifikacija glasbenih instrumentov s pomočjo nevronskih mrež : magistrsko delo
Authors:ID Lebar, Jure (Author)
ID Vlaj, Damjan (Mentor) More about this mentor... New window
ID Močnik, Grega (Comentor)
Files:.pdf MAG_Lebar_Jure_2025.pdf (4,08 MB)
MD5: 87E6E8A94D0300CD2E54085C2B9383F3
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V magistrskem delu je obravnavana klasifikacija glasbenih instrumentov s pomočjo nevronskih mrež. Primerjani so bili trije modeli: enodimenzionalna konvolucijska mreža (1D CNN), dvodimenzionalna konvolucijska mreža (2D CNN) in rekurentna nevronska mreža (LSTM). Kot vhodni podatki so bili uporabljeni mel-spektrogrami zvočnih posnetkov desetih različnih instrumentov. Rezultati kažejo, da je 2D CNN dosegla najboljšo natančnost pri klasifikaciji, medtem ko je LSTM imel največ napak, a je kljub temu dosegel solidne rezultate.
Keywords:nevronske mreže, klasifikacija instrumentov, konvolucijska mreža, rekurentna nevronska mreža
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[J. Lebar]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (XI, 80 str.))
PID:20.500.12556/DKUM-94844 New window
UDC:004.8.032.26:780.6(043.2)
COBISS.SI-ID:259791363 New window
Publication date in DKUM:22.09.2025
Views:150
Downloads:48
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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Licences

License:CC BY-SA 4.0, Creative Commons Attribution-ShareAlike 4.0 International
Link:http://creativecommons.org/licenses/by-sa/4.0/
Description:This Creative Commons license is very similar to the regular Attribution license, but requires the release of all derivative works under this same license.
Licensing start date:28.08.2025

Secondary language

Language:English
Title:Classification of musical instruments using neural networks
Abstract:This master’s thesis deals with the classification of musical instruments using neural networks. Three models were compared: one-dimensional convolutional neural network (1D CNN), two-dimensional convolutional neural network (2D CNN), and recurrent neural network (LSTM). Mel-spectrograms of audio recordings of ten different instruments were used as data input. The results show that the 2D CNN achieved the highest classification accuracy, while the LSTM produced the largest number of errors, yet still reached solid results.
Keywords:neural networks, instrument classification, convolutional network, recurrent neural network


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