Similarity in music is a concept with significant impact on ethnomusicology studies, music recommendation systems, and music information retrieval systems such as Shazam and SoundHound. Various computer-based melody similarity measures have been proposed, but comparison and evaluation of similarity measures is inherently difficult due to the subjective and application-dependent nature of similarity in music. In this thesis, we address the diversity of the problem by defining a set of music transformations that provide the criteria for comparing and evaluating melody similarity measures. This approach provides a flexible and extensible method for characterizing selected facets of melody similarity, because the set of music transformations can be tailored to the user and to the application. We demonstrate this approach using three music transformations (transposition, tempo rescaling, and selected forms of ornamentation) to compare and evaluate several existing similarity measures, including String Edit Distance measures, Geometric measures, and N-Gram based measures. We also evaluate a newly implemented distance measure, the Beat and Direction Distance Measure, which is designed to have greater awareness of the beat hierarchy and better responsiveness to ornamentation. Training and test data is drawn from music incipits from the RISM A/II collection, and ground truth is taken from the MIREX 2005 Symbolic Melodic Similarity task. Our test results show that similarity measures that are responsive to music transformations generally have better agreement with human generated ground truth. / Thesis (Master, Computing) -- Queen's University, 2012-08-31 11:03:01.167