Name
Abstract | ||
|---|---|---|
Convolutive blind source separation (BSS) aims at separating point sources from mixtures picked up by several sensors. In real-world environments moving speakers, background noise and long reverberation are encountered which often degrade the performance of BSS algorithms. In such cases, the application of a post-filter can improve the output signal quality by suppression of residual crosstalk and of background noise. In this paper we propose a novel technique to estimate the necessary power spectral densities of the cross-talk components and present a robust system which allows to further suppress both, the remaining interference from point sources and the background noise. Experimental results show the benefit of this post-processing method in realistic environments. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1661206 | 2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13 |
Keywords | Field | DocType |
background noise,power spectral density,reverberation,signal processing,degradation,convolution,point source,blind source separation,crosstalk | Residual,Signal processing,Background noise,Reverberation,Pattern recognition,Convolution,Computer science,Artificial intelligence,Interference (wave propagation),Blind signal separation,Source separation | Conference |
ISSN | Citations | PageRank |
1520-6149 | 8 | 0.72 |
References | Authors | |
6 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Aichner | 1 | 38 | 5.64 |
| M. Zourub | 2 | 8 | 0.72 |
| Herbert Buchner | 3 | 435 | 40.57 |
| W. Kellermann | 4 | 686 | 71.03 |