This thesis presents a new approach to blind deconvolution algorithms. The proposed method is a combination of a classical blind deconvolution subspace method and a marginalized particle filter. It is shown that the new method provides better performance than just a marginalized particle filter, and better robustness than the classical subspace method. The properties of the new method make it a candidate for further exploration of its potential application in acoustic blind dereverberation. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/22290 |
| Date | 09 1900 |
| Creators | Maryan, Krzysztof S. |
| Contributors | Reilly, J., Electrical and Computer Engineering |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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