A filtering approach to underdetermined blind source separation with application to temporomandibular disorders
This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the source...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Default Conference proceeding |
| Published: |
2006
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/2134/5564 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the sources is achieved by employing the l1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the sources, without utilizing the l1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs) |
|---|