Mohammad Shams Esfand Abadi - ph.d . Stu
Sabalan Danechvar - ph.D . Student, Department of Electrical Engineering , Tarbiat Modares University . Tehran, University
Mojtaba Lotfizad - Assistant Professor, Department of Electrical Engineering , Tarbiat Modares University . Tehran, University
Ali Mahlooji Far - Assistant Professor, Department of Electrical Engineering , Tarbiat Modares University . Tehran, University

In many application of noise cancellation the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms , which converge rapidly . Least mean square (LMS) adaptive filters have been used in a wide range of signal processing applications because of its simplicity in computation and implementation. the Recursive Least Squares (RLS) algorithm has established iteself as the "ultimate" aaptive filtering algorithm in the sense that it is adaptive filter exhibiting the best convergence behavior. unfortunately, practical implemenrations pf the olghorithm are often associated with high computational complexity and / or poor numerical properties. recently adaptive filtering are presented that are based on Matching Pursuits , have a nice tradeoff between complexity and performance . this paper describes a new approach for noise cancellation using the Recursive Adaptive Matching Pursuit (RAMO) Structure for attenuating noise in speeh signals. the RAMP algorithm is shown to perform very well in attenuaring noise.

Adaptive Filter , Recursive Adaptive Matching Pursuit (RAMP) , Noise Cancellation