Date of Award

1-1-2009

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Computer Science

Content Description

1 online resource (viii, 160 pages) : PDF file, illustrations, music

Dissertation/Thesis Chair

Seth D Chaiken

Committee Members

George Berg, Robert Gluck, Andrew Haas

Keywords

find, hum, match, melody, query, song, Music, Machine learning

Subject Categories

Artificial Intelligence and Robotics | Computer Sciences | Music

Abstract

We implement and evaluate a machine learning approach to improve systems for searching a database of music via melodic sample. We explore symbolic and aural input queries and test our prototypes with extensive user surveys. Our main contribution is to combine the following four elements. First is to create a unique musical abstraction that accounts for both pitch and rhythm in a relative manner. Second, our system allows for approximate matching of imperfect queries via the utilization of the Smith-Waterman algorithm that was originally designed for approximate matching of molecular subsequences, such as DNA samples. Third is to design our experiments such that every query is a `known item search'. Fourth and finally, we employ machine learning algorithms that modify the parameters of the Smith-Waterman algorithm and improve the performance of our system.

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