Date Thesis Awarded
Honors Thesis -- Access Restricted On-Campus Only
Bachelors of Science (BS)
Mark K. Hinders
Greg Jerome Bowers
This thesis describes the application of wavelet fingerprinting as a technique to analyze and automatically detect flaws in recorded audio. Specifically, it focuses on time-localized errors in digitized wax cylinder recordings and contemporary digital media. By taking the continuous wavelet transform of various recordings, we created a two-dimensional binary display of audio data. After analyzing the images, we implemented an algorithm to automatically detect where a flaw occurs by comparing the image matrix against the matrix of a known flaw. We were able to use this technique to automatically detect time-localized clicks, pops, and crackles in both cylinders and digital recordings. We also found that while other extra- musical noises, such as coughing, did not leave a traceable mark on the fingerprint, they were distinguishable from samples without the error.
Laney, Ryan, "Automatic Detection of Flaws in Recorded Music using Wavelet Fingerprinting" (2011). Undergraduate Honors Theses. William & Mary. Paper 354.
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