Performance Comparison of Adaptive Algorithms for Noise Cancellation

Adaptive filtering is widely researched topic in the present era of communication. When the received signal is continuously corrupted by noise or interference where both the noise and signal changes continuously, then arises the need for adaptive filtering. This paper deals with adaptive noise cancellation which is an alternative technique for estimating the signals corrupted by additive noise or interference. An adaptive filter has the property that its frequency response is adjustable or modifiable automatically to improve its performance in accordance with some criterion, allowing the filter to adapt changes in the input signal characteristics. Moreover, adaptive filters have the capability of adaptively tracking the signal under non-stationary conditions it is widely used in many applications because of their self adjusting performance. In this paper we compared performances of adaptive algorithms (LMS, NLMS and SELMS) for noise cancellations. Performance analysis of all three algorithms is presented in term of MSE, SNR before filtering and after filtering, computational complexity and stability. The result of MATLAB simulation shows that our approach provides a good comparison without much degradation in its performance.