Name
Title | ||
|---|---|---|
Frequency domain multi-channel noise reduction based on the spatial subspace decomposition and noise eigenvalue modification |
Abstract | ||
|---|---|---|
In this paper, frequency domain multi-channel noise reduction algorithms are proposed, based on the subspace decomposition of narrow-band spatial covariance matrices. In speech-present periods, the multi-channel input signals are decomposed into speech and noise spatial subspaces. The noise eigenvalues are modified in order to update the noise statistics not only in the noise-only period but also in the speech-present period. Three approaches are introduced for the noise eigenvalue modification, which are based on the rank-1 property of the speech narrow-band spatial covariance matrix for the single speech source. The proposed algorithms are tested with the simulated data and real data, and the results show that the proposed methods yield better performance compared to the conventional multi-channel Wiener filtering and the time domain subspace approaches. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.specom.2007.11.004 | Speech Communication |
Keywords | Field | DocType |
conventional multi-channel wiener,noise spatial subspaces,multi-channel input signal,multi-channel filtering,noise eigenvalues,spatial subspace decomposition,noise statistic,noise reduction,speech-present period,subspace decomposition,frequency domain multi-channel noise,proposed algorithm,noise eigenvalue modification,eigenvalues,frequency domain,wiener filter,time domain,covariance matrix | Frequency domain,Wiener filter,Noise reduction,Value noise,Covariance function,Noise measurement,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Gaussian noise,Gradient noise | Journal |
Volume | Issue | ISSN |
50 | 5 | Speech Communication |
Citations | PageRank | References |
3 | 0.42 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gibak Kim | 1 | 103 | 7.38 |
| Nam Ik Cho | 2 | 712 | 106.98 |