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Title:Glasovna ključavnica na platformi Raspberry Pi
Authors:ID Pušnik, Nejc (Author)
ID Holobar, Aleš (Mentor) More about this mentor... New window
Files:.pdf MAG_Pusnik_Nejc_2017.pdf (4,01 MB)
MD5: 186E60AFFC9AEC04F48C23E4F9A52CFF
PID: 20.500.12556/dkum/0fd259a0-fdaf-4650-b30b-7014847a9e35
 
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 smo izdelali glasovno ključavnico na platformi Raspberry Pi. V programskem jeziku Java smo izdelali program, ki s pomočjo mikrofona zajema zvočni signal in iz njega izlušči koeficiente melodičnega kepstruma. Nato smo na podlagi razdalje, izračunane z algoritmom dinamičnega časovnega prileganja, med seboj primerjali in klasificirali posnetke izgovorjave 49 slovenskih besed sedmih različnih oseb. Analizirali smo vpliv števila koeficientov melodičnega kepstruma, dolžine izgovorjene besede, števila samoglasnikov v izgovorjeni besedi, spola govorcev in šuma. Pri posnetkih z razmerjem signal–šum 25 dB je najmanjša dobljena napaka razpoznave znašala 9,45 %, pri posnetkih z razmerjem signal–šum 15 dB pa približno 26,97 %.
Keywords:Glasovna ključavnica, Raspberry Pi, dinamično časovno prileganje, koeficienti melodičnega kepstruma.
Place of publishing:[Maribor
Publisher:N. Pušnik
Year of publishing:2017
PID:20.500.12556/DKUM-66930 New window
UDC:004.357:004.934.8'1(043.2)
COBISS.SI-ID:20969750 New window
NUK URN:URN:SI:UM:DK:ODZFVYZ8
Publication date in DKUM:17.10.2017
Views:1677
Downloads:178
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:18.07.2017

Secondary language

Language:English
Title:Voice Lock on Raspberry Pi Platform
Abstract:In the master's thesis, we created a voice lock on the Raspberry Pi platform. In the Java programming language, we created a program that uses a microphone to capture an acoustic signal and extracts the Mel-frequency cepstral coefficients from it. Then, on the basis of the distance, calculated by the dynamic time-matching algorithm, we compared and classified the recordings of the pronunciation of 49 Slovene words by seven different people. We analysed the influence of the number of Mel-frequency cepstral coefficients, the length of the spoken word, the number of vowels in the spoken word, speaker’s gender and noise. For the recordings with a signal-to-noise ratio of 25 dB the minimum detection error was 9.45%, whereas for the recordings with a signal-to-noise ratio of 15 dB detection error increased to 26.97%.
Keywords:Voice lock, Raspberry Pi, dynamic time warping, mel frequency cepstral coefficient.


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