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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.
About the author
Markus Schedl graduated in Computer Science from the ViennaUniversity of Technology in 2004. He earned his PhD inComputational Perception in 2008 from the Johannes KeplerUniversity Linz, where he is employed as assistant professor. Hismain research interests include Web Mining, Music InformationRetrieval, and Information Visualization.
