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Title:
SAMODEJNA KLASIFIKACIJA GLASBENIH ŽANROV ZVOČNIH POSNETKOV
Authors:
ID
Rupnik, Dal
(Author)
ID
Holobar, Aleš
(Mentor)
More about this mentor...
Files:
MAG_Rupnik_Dal_2013.pdf
(3,50 MB)
MD5: 9E2B388C8606473DCED84730EFC2E2F0
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 predstavimo sistem, ki na mobilni platformi iOS samodejno klasificira glasbeni žanr zvočnih posnetkov na podlagi zajetih vrednosti značilnic. Sistem za posamezen posnetek izračuna vektor značilnic, ki opredeljujejo ritmične, tonske in energetske lastnosti posnetka. Na osnovi učne množice vektorjev z označenim glasbenim žanrom se sistem nauči značilnosti posameznih žanrov, na podlagi le teh pa kasneje opravlja klasifikacijo testnih posnetkov, ki nimajo označenega glasbenega žanra. Klasifikacijo smo izvedli z metodo podpornih vektorjev in pri tem na 1.000 testnih posnetkih dosegli 64 % natančnost ločevanja med naslednjimi desetimi žanri: blues, klasična glasba, country, disco, hip hop, jazz, metal, pop, reggae in rock.
Keywords:
klasifikacija glasbenih žanrov
,
analiza posnetka
,
značilnice glasbe
,
strojno učenje
,
metoda podpornih vektorjev
,
mobilna platforma
Place of publishing:
Maribor
Publisher:
[D. Rupnik]
Year of publishing:
2013
PID:
20.500.12556/DKUM-41907
UDC:
004.9'1(043.2)
COBISS.SI-ID:
17346326
NUK URN:
URN:SI:UM:DK:1MNPJ3X9
Publication date in DKUM:
13.09.2013
Views:
2482
Downloads:
226
Metadata:
Categories:
KTFMB - FERI
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:
RUPNIK, Dal, 2013,
SAMODEJNA KLASIFIKACIJA GLASBENIH ŽANROV ZVOČNIH POSNETKOV
[online]. Master’s thesis. Maribor : D. Rupnik. [Accessed 25 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=41907
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Secondary language
Language:
English
Title:
CLASSIFICATION OF AUDIO SIGNALS INTO MUSIC GENRES
Abstract:
In this work, we present a mobile application for iOS, which determines musical genre of audio signal based on the features extracted from the signal. For each audio signal a feature vector that represents timbral, rhythmic and energetic properties of the signal is calculated. Based on a training set of feature vectors with labelled musical genre, system learns the characteristics of specific genre and uses this information to classify unlabelled audio signals into multiple musical genres. Classification is performed by support vector machine unsupervised machine learning algorithm. When tested on 1,000 audio signals with 10 different genres, the implemented classifier yielded accuracy of 64 %.
Keywords:
music genre classification
,
feature extraction
,
audio signal analysis
,
machine learning
,
support vector machine
,
moblie platform
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