Melodic Segmentation on Different Musical Genres

To create a sufficient repository of test data for our model-based implementations of music information retrieval functions working on symbolic documents, we have used three different approaches to melodic segmentation on monophonic pieces with a broad range of genres from baroque/classical music to pop/rock. All three methods are driven by musical knowledge (in contrast to methods such as n-gram segmentation). Two of the algorithms we have applied are taken from former work of other researchers, the third algorithm has been developed in our department and will be introduced briefly. Our repository of test documents (midi format) consisted of 52 files making it more representative (2.5 to 5 times the number of documents)than those that have been referenced in previous publications. This paper describes our experiences with the applied algorithms, the results that have been achieved, and the conclusions we have been able to draw for improving music segmentation methods.