Name
Title | ||
|---|---|---|
A Comparative Study of Blind Speech Separation Using Subspace Methods and Higher Order Statistics |
Abstract | ||
|---|---|---|
In this paper we report the results of a comparative study on blind speech signal separation approaches. Three algorithms, Oriented Principal Component Analysis (OPCA), High Order Statistics (HOS), and Fast Independent Component Analysis (Fast-ICA), are objectively compared in terms of signal-to-interference ratio criteria. The results of experiments carried out using the TIMIT and AURORA speech databases show that OPCA outperforms the other techniques. It turns out that OPCA can be used for blindly separating temporal signals from their linear mixtures without need for a pre-whitening step. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10546-3_15 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Blind source separation,speech signals,second-order statistics,Oriented Principal Component Analysis | TIMIT,Subspace topology,Pattern recognition,Computer science,Higher-order statistics,Speech recognition,Artificial intelligence,Independent component analysis,Order statistic,Blind signal separation,Blind speech separation,Principal component analysis | Conference |
Volume | ISSN | Citations |
61 | 1865-0929 | 1 |
PageRank | References | Authors |
0.38 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yasmina Benabderrahmane | 1 | 4 | 1.51 |
| Sid-Ahmed Selouani | 2 | 124 | 33.39 |
| Douglas O’Shaughnessy | 3 | 10 | 3.37 |
| habib hamam | 4 | 124 | 23.13 |