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Full-Text Articles in Physical Sciences and Mathematics
Musical Query-By-Content Using Self-Organizing Maps, Kyle B. Dickerson
Musical Query-By-Content Using Self-Organizing Maps, Kyle B. Dickerson
Theses and Dissertations
The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems, however, allow users to search based upon the actual acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms and are often confined to a single query style (e.g., humming). These methods also lose much of the …
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Machine Learned Melody Matching Using Strictly Relative Musical Abstractions, Michael Joseph Kolta
Legacy Theses & Dissertations (2009 - 2024)
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 …