Computational Models of Music Similarity and their Application in Music Information Retrieval

This thesis aims at developing techniques which support users in accessing and discovering music. The main part consists of two chapters. Chapter 2 gives an introduction to computational models of music similarity. The combination of different approaches is optimized and the largest evaluation of music similarity measures published to date is presented. The best combination performs significantly better than the baseline approach in most of the evaluation categories. A particular effort is made to avoid overfitting. To cross-check the results from the evaluation based on genre classification a listening test is conducted. The test confirms that genrebased evaluations are suitable to efficiently evaluate large parameter spaces. Chapter 2 ends with recommendations on the use of similarity measures. Chapter 3 describes three applications of such similarity measures. The first application demonstrates how music collections can be organized and visualized so that users can control the aspect of similarity they are interested in. The second application demonstrates how music collections can be organized hierarchically into overlapping groups at the artist level. These groups are summarized using words from web pages associated with the respective artists. The third application demonstrates how playlists can be generated which require minimum user input.