M.Sc. RESEARCH THESIS LARGE-SCALE MUSIC CLASSIFICATION USING AN APPROXIMATE ...

106 Pages·2011·5.65 MB·English

LARGE-SCALE MUSIC CLASSIFICATION
USING AN APPROXIMATE k-NN
CLASSIFIER
Haukur Pálmason
Master of Science
Computer Science
January 2011
School of Computer Science
Reykjavík University
M.Sc. RESEARCH THESIS
ISSN1670-8539

Large-Scale Music Classification using
an Approximate k-NN Classifier
by
Haukur Pálmason
Research thesis submitted to the School of Computer Science
at Reykjavík University in partial fulfillment of
the requirements for the degree of
Master of Science in Computer Science
January 2011
Research Thesis Committee:
Dr. Björn Þór Jónsson, Supervisor
Associate Professor, Reykjavík University, Iceland
Dr. Laurent Amsaleg
Research Scientist, IRISA-CNRS, France
Dr. Yngvi Björnsson
Associate Professor, Reykjavík University, Iceland
Copyright
Haukur Pálmason
January 2011
Large-Scale Music Classification using
an Approximate k-NN Classifier
Haukur Pálmason
January 2011
Abstract
Content based music classification typically uses low-level feature vectors of
high-dimensionality to represent each song. Since most classification meth-
ods used in the literature do not scale well, the number of such feature vec-
tors is usually reduced in order to save computational time. Approximate
k-NN classification, on the other hand, is very efficient and copes better with
scale than other methods. It can therefore be applied to more feature vec-
tors within a given time limit, potentially resulting in better quality. In this
thesis, we first demonstrate the effectiveness and efficiency of approximate
k-NN classification through a traditional genre classification task, achieving
very respectable accuracy in a matter of minutes. We then apply the approx-
imate k-NN classifier to a large-scale collection of more than thirty thousand
songs. We show that, through an iterative process, the classification con-
verges relatively quickly to a stable state. We also show that by weighing the
classifier with the size of genres, genre distribution in the classification result
is improved considerably.
Flokkun stórra tónlistargrunna með k-NN nálgunarflokkun
Haukur Pálmason
Janúar 2011
Útdráttur
Venjan er að sjálfvirk tegundarflokkun tónlistar byggð á innihaldi noti marg-
víða lýsinga fyrir hvert lag. Þar sem fæstar gerðir flokkunar skalast vel er
algengast að nota fáa slíkar lýsinga til að spara tíma. k-NN nálgunarflokkun
er hins vegar skilvirk aðferð sem ræður betur við stór gagnasöfn en mar-
gar aðrar aðferðir. Þess vegna er hægt að nota slíka flokkun á fleiri lýsinga
innan ákveðinna tímamarka, sem hugsanlega skilar sér í meiri gæðum. Í
þessari ritgerð sýnum við fyrst fram á árangur og skilvirkni notkunar k-NN
nálgunarflokkunar á hefðbundna tónlistartegundarflokkun, þar sem við náum
mjög prýðilegum árangri á nokkrum mínútum. Við keyrum síðan k-NN
nálgunarflokkun á stórt lagasafn með yfir þrjátíu þúsund lögum. Við sýnum
fram á að með ítruðu ferli beinist tónlistarsafnið fljótt yfir í stöðuga flokka.
Einnig sýnum við fram á að með því að gefa flokkuninni mismunandi vægi
eftir stærð hvers tegundarflokks, bætum við dreifingu tegundarflokka í niður-
stöðunum umtalsvert.
Tomywife,SigurbjörgÓskSigurðardóttir,andmythreekids,
StefánElíHauksson,MikaelBlærHaukssonandÍsabellaSólHauksdóttir.

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Acknowledgements
Dr. BjörnÞórJónsson,mysupervisor,forconvincingmetotakeonthisproject,andfor
allhishelpinturningthisintoreality.
Dr. LaurentAmsaleg,forhiscontributionsasco-authoroftherelatedpaper.
Grímur Tómas Tómasson, a fellow M.Sc. student at the Database Lab for invaluable
helpwithcodinganddebugging.
TheReykjavíkUniversityDevelopmentFund,foraprojectgrantsupportingthiswork.
The Icelandic Research Fund for Graduate Students, for a project grant supporting
thiswork.
Tonlist.is,forgrantingusresearchaccesstotheircollectionofIcelandicsongs.
DavíðEinarsson,anemployeeatTonlist.is,forallhishelp.
Dr. George Tzanetakis, for granting access to his song collection, and for quickly an-
sweringallquestionsregardingtheMARSYASframework.
Icelandicmusicians,whoparticipatedinmyground-truthexperiment.
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