Mining the Web for Music Artist-Related Information: Automatically Extracting, Analyzing, and Visualizing Information on Music Artists from the World Wide Web - Couverture souple

Schedl, Markus

 
9783838100821: Mining the Web for Music Artist-Related Information: Automatically Extracting, Analyzing, and Visualizing Information on Music Artists from the World Wide Web
Couverture souple
ISBN 10 : 3838100824 ISBN 13 : 9783838100821
Editeur : Suedwestdeutscher Verlag fuer Hochschulschriften, 2009

Synopsis

Music-related metadata is becoming more and more important in times of digital music distribution. Methods for automatically extracting such information from the WWW have been elaborated, implemented, and analyzed. On sets of Web pages that are related to a music artist or band, Web content mining techniques are applied to address the following categories of information: similarities between music artists, prototypicality of an artist for a genre, descriptive properties of an artist, band members and instrumentation, images of album cover artwork. Different approaches to retrieve the corresponding pieces of information for each of these categories have been elaborated and evaluated thoroughly on a considerable variety of music repositories. Moreover, visualization methods and user interaction models for prototypical and similar artists as well as for descriptive terms will be presented. Based on the insights gained by the conducted experiments, the core application of this thesis, the Automatically Generated Music Information System (AGMIS) was build. AGMIS demonstrates the applicability of the elaborated techniques on a large collection of more than 600,000 artists.

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Présentation de l'éditeur

Music-related metadata is becoming more and more important in times of digital music distribution. Methods for automatically extracting such information from the WWW have been elaborated, implemented, and analyzed. On sets of Web pages that are related to a music artist or band, Web content mining techniques are applied to address the following categories of information: similarities between music artists, prototypicality of an artist for a genre, descriptive properties of an artist, band members and instrumentation, images of album cover artwork. Different approaches to retrieve the corresponding pieces of information for each of these categories have been elaborated and evaluated thoroughly on a considerable variety of music repositories. Moreover, visualization methods and user interaction models for prototypical and similar artists as well as for descriptive terms will be presented. Based on the insights gained by the conducted experiments, the core application of this thesis, the Automatically Generated Music Information System (AGMIS) was build. AGMIS demonstrates the applicability of the elaborated techniques on a large collection of more than 600,000 artists.

Biographie de l'auteur

Markus Schedl graduated in Computer Science from the Vienna University of Technology in 2004. He earned his PhD in Computational Perception in 2008 from the Johannes Kepler University Linz, where he is employed as assistant professor. His main research interests include Web Mining, Music Information Retrieval, and Information Visualization.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
Suedwestdeutscher Verlag fuer Hochschulschriften
Date d'édition
2009
Langue
anglais
ISBN 10 
3838100824
ISBN 13 
9783838100821
Reliure
Broché
Nombre de pages
172
Coordonnées du fabricant
non disponible
Personne responsable
non disponible