Samodejna klasifikacija glasbenih žanrov zvočnih posnetkov

magistrsko delo

Dal Rupnik (Author), Aleš Holobar (Mentor)

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;

Data

Language:	Slovenian
Year of publishing:	2013
Typology:	2.09 - Master's Thesis
Organization:	UM FERI - Faculty of Electrical Engineering and Computer Science
Publisher:	[D. Rupnik]
UDC:	004.9'1(043.2)
COBISS:	17346326
Views:	1711
Downloads:	200
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	CLASSIFICATION OF AUDIO SIGNALS INTO MUSIC GENRES
Secondary 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 %.
Secondary keywords:	music genre classification;feature extraction;audio signals analysis;machine learning;support vector machine;mobile platform;
URN:	URN:SI:UM:
Type (COBISS):	Master's thesis/paper
Thesis comment:	Univ. v Mariboru, Fak. za elektrotehniko, računalništvo in informatiko
Pages:	XIV, 71 f.
ID:	8726180