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Full-Text Articles in Physical Sciences and Mathematics
Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern
Analysis Of Music Genre Clustering Algorithms, Samuel Walter Stern
Theses and Dissertations
Classification and clustering of music genres has become an increasingly prevalent focusin recent years, prompting a push for research into relevant algorithms. The most successful algorithms have typically applied the Naive Bayes or k-Nearest Neighbors algorithms, or used Neural Networks to perform classification. This thesis seeks to investigate the use of unsupervised clustering algorithms such as K-Means or Hierarchical clustering, and establish their usefulness in comparison to or conjunction with established methods.
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 …