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Re: Computational ASA

Jon Boley wrote:

I know there are several CASA experts on this list, so I'd like to ask why source separation seems to be so difficult.  It's seems like the general consensus is that source separation is far too difficult, and research has focused on understanding features within a mix.  Yet, from what I've read, current methods of feature extraction work quite well.  It only seems natural that we could write an algorithm that groups these features according to their perceived source and creates separate audio streams based on this information.  While this would be much more difficult in noisy or reverberant environments, I would imagine it would be quite simple in a less complex environment.
What is it that makes source separation so difficult?

The problem is that reliable feature detection is hard when intrusion is
strong. Also,
even if a feature is successfully detected, say a pitch point, how can
one find its
"perceived source"?

The CASA problem is almost as hard as the whole of computational
audition, which is
probably as hard as the whole of machine perception.

Another site you can learn a little more about this is


DeLiang Wang

Prof. DeLiang Wang
Department of Computer Science and Engineering
The Ohio State University
2015 Neil Ave.
Columbus, OH 43210-1277, U.S.A.

Email: dwang [at] cis [dot] ohio-state [dot] edu
Phone: 614-292-6827 (OFFICE); 614-292-7402 (LAB)
Fax: 614-292-2911
URL: http://www.cse.ohio-state.edu/~dwang