Name
Abstract | ||
|---|---|---|
We propose in this paper a new strategy for non-stationary signals denoising based on designing a time-varying filter adapted to the signal short term spectral characteristics. The basic idea leading us to use a new parametric nonlinear weighting of the measured signal short term spectral amplitude (STSA) is exposed. The overall system consists in combining the estimated STSA and the complex exponential of the noisy phase. The proposed technique results in a significant reduction of the noise for a variety of non-stationary signals including speech signals |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICASSP.2006.1660692 | Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference |
Keywords | Field | DocType |
Fourier transforms,nonlinear functions,signal denoising,speech processing,time-varying filters,nonlinear weighting function,nonstationary signal denoising,short time Fourier transform,signal short term spectral amplitude,signal short term spectral characteristics,speech signals,time-varying filter | Noise reduction,Speech processing,Weighting,Nonlinear system,Pattern recognition,Computer science,Stationary process,Short-time Fourier transform,Fourier transform,Parametric statistics,Artificial intelligence | Conference |
Volume | ISSN | ISBN |
3 | 1520-6149 | 1-4244-0469-X |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farès Abda | 1 | 0 | 0.34 |
| David Brie | 2 | 130 | 24.28 |
| Radu Ranta | 3 | 37 | 9.35 |