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so i have this assignment to use blind deconvolution to clean up an audio recording. I read a lot about this topic and found one using temporal predictability(the best i've found so far). But information on this topic is very scarce and I need some fresh ideas. Are there any good techniques to approach this problem?. On a side note, could a wiener filter be used for this?
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Joined: Sat Aug 30 2008, 09:57PM
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implement frequency-domain deconvolution first (it's faster than time-domain (de)convolution when using FFT) and then use bayes statistics to guess the kernel function. (check out Angelo Farina's papers on convolution reverb, he did amazing stuff in this field).
Sorry for not being precise about what to do, I only stumbled upon deconvolution some time ago. Wiener Filter seem to do a good job, so yeah, why not give them a try.
implement frequency-domain deconvolution first (it's faster than time-domain (de)convolution when using FFT) and then use bayes statistics to guess the kernel function. (check out Angelo Farina's papers on convolution reverb, he did amazing stuff in this field).
Sorry for not being precise about what to do, I only stumbled upon deconvolution some time ago. Wiener Filter seem to do a good job, so yeah, why not give them a try.
that's a lot of help. i searched for bayes statistics and found a lot of info. Now I have something to start off with:-)
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Need some help approaching blind deconvolution problem
