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Evaluating Melodic Similarity Using Pairwise Sequence Alignments and Suffix Trees

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Title: Evaluating Melodic Similarity Using Pairwise Sequence Alignments and Suffix Trees
Author: Wickland, David D.
Department: School of Computer Science
Program: Computer Science
Advisor: Calvert, David A.
Abstract: The music industry was the first mass-media industry to be transformed by the transition to digital. Today, music consumers have access to personal libraries, subscription-based libraries, and online content via web-media hosts or channels. Now more than ever, there are tremendous quantities of music available to the individual on a host of devices and platforms. With the explosion in access, there comes increasing demand to describe, index, retrieve, and interact with digital music. Consequently, there is increasing demand for novel and efficient ways to compare and manipulate digital music. The goal of investigating melodic similarity is to develop ways of comparing melodies based on structural elements or patterns present in the melody. This approach differs from other music similarity measures, which typically employ low-level features, or clustering based on associations. With robust and efficient ways of evaluating melodic similarities, we can develop new approaches to interacting with digital music.
URI: http://hdl.handle.net/10214/11608
Date: 2017-09-19


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