Developing and testing sub-band spectral features in music genre and music mood machine learning

Developing and testing sub-band spectral features in music genre and music mood machine learning

In the field of artificial intelligence, supervised machine learning enables us to try to develop automatic recognition systems. In music information retrieval, training and testing such systems is possible with a variety of music datasets. Two key prediction tasks are those of music genre recogniti...

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Bibliographic Details
Main Author: Prezja, Fabi
Other Authors: Humanistis-yhteiskuntatieteellinen tiedekunta, Faculty of Humanities and Social Sciences, Musiikin, taiteen ja kulttuurin tutkimuksen laitos, Department of Music, Art and Culture Studies, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:eng
Published: 2018
Subjects:
music information retrieval
music genre classification
music mood classification
sub-band features
polyphonic timbre
spectral features
adaptive spectral window decomposition
Music, Mind and Technology (maisteriohjelma)
Master's Degree Programme in Music, Mind and Technology
3054
koneoppiminen
genret
luokitus (toiminta)
machine learning
genres
classification
Online Access: https://jyx.jyu.fi/handle/123456789/60963

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