This thesis presents a comparison between two factorization techniques { Probabilistic Latent Component Analysis (PLCA) and Non-Negative Least Squares (NNLSQ)
{ for the problem of detecting note events played by a vibraphone, using a microphone
for sound acquisition in the context of live performance. Ambient noise is reduced by
using specifi c dictionary codewords to model the noise.
The results of the factorization are analyzed by two causal onset detection algorithms: a rule-based algorithm and a trained machine learning based classi fier. These
onset detection algorithms yield decisions on when note events happen. Comparative
results are presented, considering a database of vibraphone recordings with di fferent
levels of noise, showing the conditions under which the event detection is reliable. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3960 |
| Date | 30 April 2012 |
| Creators | Zehtabi, Sonmaz |
| Contributors | Tzanetakis, George |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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