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Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier6qtX3pFwXQZG2LgkFdY/LJ4gH
Repositorysid.inpe.br/sibgrapi@80/2006/07.11.16.44
Last Update2006:07.11.16.44.01 administrator
Metadatasid.inpe.br/sibgrapi@80/2006/07.11.16.44.02
Metadata Last Update2020:02.19.03.17.34 administrator
Citation KeyKolhoffPhilippPreu▀JacquelineLoviscachJ÷rn:2006:PrGlAu
TitleMusic Icons: Procedural Glyphs for Audio Files
FormatOn-line
Year2006
Access Date2021, Jan. 25
Number of Files1
Size822 KiB
Context area
Author1 Kolhoff
2 Philipp
3 Preu▀
4 Jacqueline
5 Loviscach
6 J÷rn
Affiliation1 Hochschule Bremen (University of Applied Sciences)
EditorOliveira Neto, Manuel Menezes de
Carceroni, Rodrigo Lima
e-Mail Addressj.loviscach@computer.org
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 19 (SIBGRAPI)
Conference LocationManaus
Date8-11 Oct. 2006
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2008-07-17 14:11:02 :: j.loviscach -> administrator ::
2009-08-13 20:38:01 :: administrator -> banon ::
2010-08-28 20:02:22 :: banon -> administrator ::
2020-02-19 03:17:34 :: administrator -> :: 2006
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Keywordsmusic information retrieval, visual data mining, audio features, MFCC, neural net.
AbstractNowadays, a personal music collection may comprise thousands of MP3 files. Visualization can help the user to gain an overview and to find similar songs inside so large a set. We describe a method to create icons from audio files in such a way that songs which the user considers similar receive similar icons. This allows visual data mining in standard directory listings of window-based operating sys-tems. The icons consist of bloom-like shapes, whose form and color depend on eight parameters. These parameters are controlled through a neural net, the input of which are audio features that are extracted algorithmically from the MP3 files. To adapt the system to the users perception and interests, the neural net is initially trained with a small set of songs and icons. User studies done on the system demon-strate a strong perceptual relation between music and icons.
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data URLhttp://urlib.net/rep/6qtX3pFwXQZG2LgkFdY/LJ4gH
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/LJ4gH
Languageen
Target Filekolhoff-MusicIcons.pdf
User Groupj.loviscach
administrator
Visibilityshown
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Host Collectionsid.inpe.br/banon/2001/03.30.15.38
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Empty Fieldsaccessionnumber archivingpolicy archivist area callnumber copyholder copyright creatorhistory descriptionlevel dissemination documentstage doi edition electronicmailaddress group holdercode isbn issn label lineage mark mirrorrepository nextedition nexthigherunit notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url versiontype volume

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