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Title:Brezizgubno stiskanje avdio posnetkov z nevronskimi mrežami : magistrsko delo
Authors:ID Železnik, Luka (Author)
ID Strnad, Damjan (Mentor) More about this mentor... New window
ID Podgorelec, David (Comentor)
Files:.pdf MAG_Zeleznik_Luka_2025.pdf (2,74 MB)
MD5: BEE057F2913CBA2D2E558A8AAE4E6A2B
 
.zip MAG_Zeleznik_Luka_2025.zip (146,51 KB)
MD5: 6889F2627B5C4DC46B4310B9F221A877
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrska naloga se začne s kratkim pregledom relevantnih arhitektur nevronskih mrež in obstoječih brezizgubnih metod stiskanja avdia. Nato je predstavljena nova metoda za brezizgubno stiskanje avdia, ki temelji na napovedovanju naslednjega avdio vzorca s pomočjo konvolucijske nevronske mreže. Mreža se za vsak vhodni avdio posnetek uči posebej. Sledijo optimizacija hiperparametrov in nastavitev algoritma ter primerjava predlagane metode z obstoječimi algoritmi.
Keywords:stiskanje, algoritem, entropija, Golomb-Riceovo kodiranje, strojno učenje
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[L. Železnik]
Year of publishing:2025
Number of pages:1 spletni vir (1 datoteka PDF (X, 68 str.))
PID:20.500.12556/DKUM-91490 New window
UDC:004.627:004.8.021(043.2)
COBISS.SI-ID:226893315 New window
Publication date in DKUM:06.02.2025
Views:188
Downloads:74
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:07.01.2025

Secondary language

Language:English
Title:Lossless audio compression with neural networks
Abstract:The thesis begins with a brief overview of relevant neural network architectures and existing lossless audio compression methods. It then introduces a novel method for lossless audio compression, based on predicting the next audio sample using a convolutional neural network. The network is trained individually for each input audio recording. The research continues with optimizing algorithm hyperparameters and settings, followed by a comparison of the proposed method with existing algorithms.
Keywords:compression, algorithm, entropy, Golomb-Rice coding, machine learning


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