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Title:Konvolucijske nevronske mreže za odkrivanje napak s pomočjo zvoka : magistrsko delo
Authors:Fažmon, Gorazd (Author)
Golob, Marjan (Mentor) More about this mentor... New window
Files:.pdf MAG_Fazmon_Gorazd_2020.pdf (1,97 MB)
MD5: FB56F5CD05A78AEFE2E2E44F18E7ECA4
 
Language:Slovenian
Work type:Master's thesis/paper (mb22)
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
Year of publishing:2020
Place of performance:Maribor
Publisher:[G. Fažmon]
Number of pages:X, 72 f.
Source:Maribor
UDC:681.586.4.09043.2)
COBISS_ID:36699139 New window
NUK URN:URN:SI:UM:DK:BHDEHLAS
Views:137
Downloads:29
Metadata:XML RDF-CHPDL 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: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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