"Computational models of Music Similarity and their Application in Music Information Retrieval"
Computational models of Music Similarity and their Application in Music Information Retrieval
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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.