Title page for ETD etd-12282006-020030


Type of Document Master's Thesis
Author Bond, Zachary
Author's Email Address zacwbond@vt.edu
URN etd-12282006-020030
Title Unsupervised Classification of Music Signals: Strategies Using Timbre and Rhythm
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Abbott, A. Lynn Committee Chair
Beex, A. A. Louis Committee Member
Martin, Thomas L. Committee Member
Keywords
  • clustering
  • music
  • rhythm
  • timbre
  • classification
Date of Defense 2006-11-15
Availability unrestricted
Abstract
This thesis describes the ideal properties of an adaptable music classification system based on unsupervised machine learning, and argues that such a system should be based on the fundamental musical properties of timbre, rhythm, melody and harmony. The first two properties and the signal features associated with them are then explored in more depth. In the area of timbre, the relationship between musical style and commonly-extracted signal features within a broad range of piano music is explored, in an effort to identify features which are consistent among all piano music but different for other instruments. The effect of lossy compression on these same timbre features is also investigated. In the area of rhythm, a new tempo tracking tool is provided which produces a series of histograms containing beat and sub-beat information throughout the course of a musical recording. These histograms are then shown to be useful in the analysis of synthesized rhythms and real music. Additionally, a novel method based on the Expectation-Maximization algorithm is used to extract features for classification from the histograms.
Files
  Filename       Size       Approximate Download Time (Hours:Minutes:Seconds) 
 
 28.8 Modem   56K Modem   ISDN (64 Kb)   ISDN (128 Kb)   Higher-speed Access 
  results_rhythm.txt 9.91 Kb 00:00:02 00:00:01 00:00:01 < 00:00:01 < 00:00:01
  results_timbre.txt 90.29 Kb 00:00:25 00:00:12 00:00:11 00:00:05 < 00:00:01
  unsupervised_classification.pdf 522.75 Kb 00:02:25 00:01:14 00:01:05 00:00:32 00:00:02

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