Automatic Classification of Musical Artists based on Web-Data

The organization of music is one of the central challenges in times of increasing distribution of digital music. A well-tried means is the classification in genres and/or styles. In this paper we propose the use of text categorization techniques to classify artists present on the Internet. In particular, we retrieve and analyze webpages ranked by search engines to describe artists in terms of word occurrences on related pages. To classify artists we primarily use support vector machines. Based on a previously published paper and on a master’s thesis, we present experiments comprising the evaluation of the classification process on a taxonomy of 14 genres with altogether 224 artists, as well as an estimation of the impact of daily fluctuations in the Internet on our approach, exploiting a long-term study over a period of almost one year. On the basis of these experiments we study (a) how many artists are necessary to define the concept of a genre, (b) which search engines perform best, (c) how to formulate search queries best, (d) which overall performance we can expect for classification, and finally (e) how our approach is suited as a similarity measure for artists.