DISCOVERING AND VISUALIZING PROTOTYPICAL ARTISTS BY WEB-BASED CO-OCCURRENCE ANALYSIS 1 2 1 1 2 Markus Schedl ; Peter Knees Gerhard Widmer ;
[email protected] [email protected] [email protected] 1 Department of Computational Perception Johannes Kepler University (JKU) A-4040 Linz, Austria 2 Austrian Research Institute for Artificial Intelligence (ÖFAI) A-1010 Vienna, Austria ABSTRACT Furthermore, prototypical artists are very useful for vi- sualizing music repositories since they are usually well- Detecting artists that can be considered as prototypes for known. Thus, also unexperienced music listeners are able particular genres or styles of music is an interesting task. to assign them to a particular genre or style of music and In this paper, we present an approach that ranks artists ac- can use them as reference points to discover similar but cording to their prototypicality. To calculate such a rank- less known artists. One possible way of visualizing proto- ing, we use asymmetric similarity matrices obtained via typical artists and the relations to their most similar neigh- co-occurrence analysis of artist names on web pages. We bors will be shown in this paper. demonstrate our approach on a data set containing 224 The approach presented here can be used to define artists from 14 genres and evaluate the results using the complete rankings based on the prototypicality of artists. rank correlation between the prototypicality ranking and Such rankings enable further applications. For example, a ranking obtained by page counts of search queries to together with genre information, they can serve as a mea- Google that contain artist and genre.