الفهرس 
Adaptive FilterOnly 14 pages are availabe for public view
 |  | from | 88 |  |  |
| | | from | 88 | | |
Abstract
Noise cancellation in a signal is an important core area of the digital signal processing. In this work, a novel algorithm for cancelling noise from the speech signal in real time environment is proposed. In many applications of noise cancellation, the characteristics of signal may change quite fast. This requires the usage of adaptive algorithms, which converge rapidly. One of the most popular adaptive noise cancellers that often used to recover signal corrupted by additive noise is Least Mean Square (LMS) algorithm and that is due to its simplicity in implementation. But it has limitation when the desired signal is strong, that the excess mean-square error is linearly increased while increasing the desired signal power.
This results in downgraded performance when the desired signal exhibits large power fluctuations. In the proposed algorithm we use the benefits of both variable step size (VSS) LMS algorithm and Normalized Differential LMS (NDLMS) algorithm to handle this situation. One more addition of this algorithm is that it uses the concept of decomposing the long adaptive filter into low order multiple sub-filters to relieve the effect of slow convergence of that long adaptive filter. Finally, the proposed (P-VSSNDLMS) algorithm yields faster convergence with minimum mean square error in simulations which performed using real speech signal with different noise power levels.