This thesis research investigates and demonstrates the feasibility of performing computationally efficient, high-dimensional acoustic classification using Mel-frequency cepstral coefficients and independent component analysis for temporal feature extraction. A process was developed to calculate Mel-frequency cepstral coefficients from samples of acoustic data grouped by either musical genre or spoken world language. Then independent component analysis was employed to extract the higher level temporal features of the coefficients in each class. These sets of unique independent features represent themes, or patterns, over time that are the underlying signals in that class of acoustic data. The results obtained from this process clearly show that the uniqueness of the independent components for each class of acoustic information are legitimate grounds for separation and classification of the data.
Library of Congress Subject Headings
Sound--Classification; Music--Acoustics and physics--Data processing; Music--Data processing; Speech--Data processing; Principal components analysis
Department, Program, or Center
Computer Science (GCCIS)
Brock, James, "Acoustic classification using independent component analysis" (2006). Thesis. Rochester Institute of Technology. Accessed from
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