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Title:Notni zapis razpoznanih zvokov glasbil s pomočjo nevronske mreže : diplomsko delo
Authors:ID Žejn, Mark (Author)
ID Šafarič, Riko (Mentor) More about this mentor... New window
ID Uran, Suzana (Comentor)
Files:.pdf VS_Zejn_Mark_2022.pdf (8,73 MB)
MD5: 4AB7D236191BC55DDDFD0404BE117EF5
 
Language:Slovenian
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:V diplomski nalogi je predstavljen način, kako lahko glasbeno datoteko pretvorimo v valovno transformacijo, ki nam omogoča frekvenčni zapis zvoka. Tekom diplomske naloge je predstavljena klasifikacija posameznih tonov, intervalov in akordov z različnimi instrumenti (harmonika in flavta). Tak frekvenčni zapis lahko uporabljamo za učenje nevronske mreže. Nevronsko mrežo bi lahko v nadaljevanju, po uspešnem učenju uporabljali za prepoznavo posameznih tonov, intervalov, akordov in pridobljene signale frekvenčnega zapisa spreminjali v notne zapise, kar bi posamezniku omogočalo na avtomatiziran način zapisati notni zapis.
Keywords:notni zapis, nevronska mreža, glasba, pretvorba, zvok
Place of publishing:Maribor
Place of performance:Maribor
Publisher:[M. Žejn]
Year of publishing:2022
Number of pages:1 spletni vir (1 datoteka PDF (XII, 103 f.))
PID:20.500.12556/DKUM-82551 New window
UDC:004.032.26:004.4'277.2(043.2)
COBISS.SI-ID:132924419 New window
Publication date in DKUM:21.10.2022
Views:1174
Downloads:170
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:24.08.2022

Secondary language

Language:English
Title:Note recognition of musical instruments using the neural network
Abstract:In the thesis, a method is presented in which a music file can be converted into a wave transformation which allows us to record the frequency of sound. The classification of individual tones, intervals, and chords with different instruments (accordion and flute) is presented in the thesis. Such a frequency record can be used to learn a neural network. In the future, the neural network could be used to recognize individual tones, intervals, and chords after successful learning. The obtained frequency notation signals could be changed into notation. That would enable an individual to write notation in an automated way.
Keywords:notation, neural network, music, conversion, sound


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