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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier6qtX3pFwXQZG2LgkFdY/LJ4gH
Repositorysid.inpe.br/sibgrapi@80/2006/07.11.16.44
Last Update2006:07.11.16.44.01 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2006/07.11.16.44.02
Metadata Last Update2022:06.14.00.13.10 (UTC) administrator
DOI10.1109/SIBGRAPI.2006.30
Citation KeyKolhoffPhilippPreußJacquelineLoviscachJörn:2006:PrGlAu
TitleMusic Icons: Procedural Glyphs for Audio Files
FormatOn-line
Year2006
Access Date2024, Apr. 28
Number of Files1
Size822 KiB
2. Context
Author1 Kolhoff
2 Philipp
3 Preuß
4 Jacqueline
5 Loviscach
6 Jörn
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, AM, Brazil
Date8-11 Oct. 2006
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2008-07-17 14:11:02 :: j.loviscach -> administrator ::
2009-08-13 20:38:01 :: administrator -> banon ::
2010-08-28 20:02:22 :: banon -> administrator ::
2022-06-14 00:13:10 :: administrator -> :: 2006
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
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.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2006 > Music Icons: Procedural...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Music Icons: Procedural...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/6qtX3pFwXQZG2LgkFdY/LJ4gH
zipped data URLhttp://urlib.net/zip/6qtX3pFwXQZG2LgkFdY/LJ4gH
Languageen
Target Filekolhoff-MusicIcons.pdf
User Groupj.loviscach
administrator
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46RFT7E
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.08.00.20 5
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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