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DKUM
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Title:
Konvolucijske nevronske mreže za odkrivanje napak s pomočjo zvoka : magistrsko delo
Authors:
ID
Fažmon, Gorazd
(Author)
ID
Golob, Marjan
(Mentor)
More about this mentor...
Files:
MAG_Fazmon_Gorazd_2020.pdf
(1,97 MB)
MD5: FB56F5CD05A78AEFE2E2E44F18E7ECA4
PID:
20.500.12556/dkum/19f24d01-3cc8-4393-809d-726b394a3335
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 predstavljen razvoj sistema za zaznavanje napak v industrijskih procesih, ki temelji na osnovi zaznave zvoka. S pomočjo programskega orodja Audacity, so zajeti zvočni signali proizvodnih postopkov. S programskim orodjem Python je izdelan program za pretvorbo zvočnega signala v sliko. Z uporabo Python knjižnice TensorFlow je program naučen, da prepozna napako. Podan je podroben opis pomembnih pojmov, algoritmov, metod in testiranj sistema. Glavni cilj naloge je implementirati zgrajen sistem na dejanskem proizvodnem postopku.
Keywords:
konvolucijska nevronska mreža
,
kakovost zvoka
,
spektrogram
,
Mel frekvenčni kepstralni koeficienti (MFCC)
,
TensorFlow
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[G. Fažmon]
Year of publishing:
2020
Number of pages:
X, 72 f.
PID:
20.500.12556/DKUM-76761
UDC:
681.586.4.09043.2)
COBISS.SI-ID:
36699139
NUK URN:
URN:SI:UM:DK:BHDEHLAS
Publication date in DKUM:
04.11.2020
Views:
1154
Downloads:
157
Metadata:
Categories:
KTFMB - FERI
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:
FAŽMON, Gorazd, 2020,
Konvolucijske nevronske mreže za odkrivanje napak s pomočjo zvoka : magistrsko delo
[online]. Master’s thesis. Maribor : G. Fažmon. [Accessed 26 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=76761
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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:
02.07.2020
Secondary language
Language:
English
Title:
Convolutional neural networks for sound-based error detection
Abstract:
The master’s thesis presents the development of an industrial process fault detection system based on sound sensing. Using Audacity software, audio signals from production processes are captured. With the use of Python software, a program for converting audio to image is created. Using the Python TensorFlow library, the program is taught to recognize the error. A detailed description of important system concepts, algorithms, methods, and tests is given. The main objective of the task is to implement the built system on the actual production process.
Keywords:
convolutional neural network
,
sound quality
,
spectrogram
,
Mel-frequency cepstral coefficient (MFCC)
,
TensorFlow
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