vijay barfa - Academia.edu (original) (raw)
Address: Gwalior, Madhya Pradesh, India
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University of Azad Jammu & Kashmir, Muzaffarabad Pakistan [UAJK]
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Adaptive Filtering is an extreme researched topic in the present era of digital communications. W... more Adaptive Filtering is an extreme researched topic in the present era of digital communications. When the received signal is rapidly disturbed by noise where both the received signal and noise change continuously, then arises the need for adaptive filtering. The core of the adaptive filter is the adaptive algorithm. This paper deals with elimination of noise on speech signal and data signals using four algorithms-Least Mean Square (LMS) algorithm, Normalized Least Mean Squares(NLMS) algorithm, Recursive Least Squares (RLS) and Fast Transversal Filter (FTF) algorithm with implementation in MATLAB. Comparisons of the algorithms are based on forgetting factor and tap weights of filter. The algorithms chosen for implementation provide efficient performances with less computational complexity. N B) The Normalized LMS (NLMS) introduces a variable adaptation rate. It improves the convergence speed in a non-static environment. In another version, the Newton LMS, the weight update equation includes whitening in order to achieve a single mode of convergence. For long adaptation processes the Block LMS is used to make the LMS faster. In block LMS, the input signal is divided into blocks and weights are updated block wise. A simple version of LMS is called the Sign LMS. It uses the sign of the error to update the weights. Also, LMS is not a blind algorithm i.e. it requires a priori information for the reference signal.
Adaptive Filtering is an extreme researched topic in the present era of digital communications. W... more Adaptive Filtering is an extreme researched topic in the present era of digital communications. When the received signal is rapidly disturbed by noise where both the received signal and noise change continuously, then arises the need for adaptive filtering. The core of the adaptive filter is the adaptive algorithm. This paper deals with elimination of noise on speech signal and data signals using four algorithms-Least Mean Square (LMS) algorithm, Normalized Least Mean Squares(NLMS) algorithm, Recursive Least Squares (RLS) and Fast Transversal Filter (FTF) algorithm with implementation in MATLAB. Comparisons of the algorithms are based on forgetting factor and tap weights of filter. The algorithms chosen for implementation provide efficient performances with less computational complexity. N B) The Normalized LMS (NLMS) introduces a variable adaptation rate. It improves the convergence speed in a non-static environment. In another version, the Newton LMS, the weight update equation includes whitening in order to achieve a single mode of convergence. For long adaptation processes the Block LMS is used to make the LMS faster. In block LMS, the input signal is divided into blocks and weights are updated block wise. A simple version of LMS is called the Sign LMS. It uses the sign of the error to update the weights. Also, LMS is not a blind algorithm i.e. it requires a priori information for the reference signal.