A solution for achieving blind separation for underdetermined systems is to use an overcomplete basis function set that has the ability to span all possible inputs. Ideally, such a basis would be learned for each set of inputs but this is computationally expensive. A less processor intensive system is shown using a fixed dictionary of basis functions learned from existing sources and reduced using a correlation-based method. The relation between dictionary size and separation performance for underdetermined scenarios is examined and we demonstrate that a reduced dictionary can produce comparable results using less computational power.
|Translated title of the contribution||Blind signal separation using fixed overcomplete basis function dictionaries|
|Title of host publication||2003 International Symposium on Circuits and Systems, 2003 (ISCAS '03)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Pages||III-44 - III-47|
|Number of pages||4|
|Publication status||Published - May 2003|
|Event||International Symposium on Circuits and Systems, 2003 - Bangkok, Thailand|
Duration: 1 May 2003 → …
|Conference||International Symposium on Circuits and Systems, 2003|
|Abbreviated title||ISCAS '03|
|Period||1/05/03 → …|
Bibliographical noteRose publication type: Conference contribution
Sponsorship: The authors would like to thank the support from the U.K. MOD TGOl CRP Fellowship project.
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