Name
Abstract | ||
|---|---|---|
In this paper, an accurate and efficient blind source separation method based on local sparsity and K-means (LSK-BSS) is proposed. Specifically, the proposed LSK-BSS approach exploits the local sparsity of speech sources in the transformed domain to obtain closed-form solution for per-frequency mixing system estimation. On this basis, through designing superior initial points of clustering, the well-established K-means algorithm is employed to achieve accurate permutation alignment. Simulations with real reverberant speech sources show that the LSK-BSS approach yields competitive efficiency, robustness and effectiveness, in comparison with the state-of-the-arts methods. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.23919/Eusipco47968.2020.9287526 | 2020 28th European Signal Processing Conference (EUSIPCO) |
Keywords | DocType | ISSN |
Blind source separation,convolutive speech mixture,K-means,permutation ambiguity | Conference | 2219-5491 |
ISBN | Citations | PageRank |
978-1-7281-5001-7 | 0 | 0.34 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyang Huang | 1 | 0 | 0.34 |
| Ping Chu | 2 | 0 | 0.34 |
| Bin Liao | 3 | 196 | 32.33 |