Second order impropriety based complex-valued algorithm for frequency-domain blind separation of convolutive speech mixtures
The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In this context, the strong uncorrelating transform (SUT) and complex maximization of non-Gaussianity (CMN) do not produce satisfactory separation...
Saved in:
| Published in: | 2011 IEEE International Workshop on Machine Learning for Signal Processing pp. 1 - 6 |
|---|---|
| Main Authors: | , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.09.2011
|
| Subjects: | |
| ISBN: | 1457716216, 9781457716218 |
| ISSN: | 1551-2541 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The performance of the complex-valued blind source separation (BSS) is studied in the frequency domain approach to separate convolutive speech mixtures. In this context, the strong uncorrelating transform (SUT) and complex maximization of non-Gaussianity (CMN) do not produce satisfactory separation results since their assumptions about the independence among the frequency-domain complex-valued sources and the different diagonal elements of the pseudo-covariance of those sources are not met at each frequency bin. The proposed strong second order statistics (SSOS) algorithm exploits the second order impropriety of the frequency-domain complex-valued sources with the assumption that the complex-valued sources are improper and uncorrelated, and can well separate the mixtures at about 50% of frequency bins, outperforming SUT and CMN. Thus, it is promising to recover the time-domain speech sources by combing SSOS and the following indeterminacy correction in the frequency domain approach to separate convolutive speech mixtures. |
|---|---|
| ISBN: | 1457716216 9781457716218 |
| ISSN: | 1551-2541 |
| DOI: | 10.1109/MLSP.2011.6064589 |

