Using a fast RLS adaptive algorithm for efficient speech processing (original) (raw)
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Simulation and Comparative Analysis of LMS and RLS Algorithms Using Real Time Speech Input Signal
Global Journal of Research In Engineering, 2010
In practical application, the statistical characteristics of signal and noise are usually unknown or can't have been learned so that we hardly design fix coefficient digital filter. In allusion to this problem, the theory of the adaptive filter and adaptive noise cancellation are researched deeply. According to the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) algorithms realize the design and simulation of adaptive algorithms in noise canceling, and compare and analyze the result then prove the advantage and disadvantage of two algorithms .The adaptive filter with MATLAB are simulated and the results prove its performance is better than the use of a fixed filter designed by conventional methods.
Review of Noise Cancellation of Speech Signal by Using Adaptive Filtering with RLS Algorithm
2015
In communication system, if statistical property of the signal is known then we are use a fixed filter but when the property of the signal is unknown we used adaptive filter. Adaptive filter is one of the most important areas in DSP to remove background noise. In this paper, we describe the noise cancelling using recursive least square (RLS) algorithm to remove the noise from input signal. The RLS adaptive filter uses as a reference signal on the input port and desired signal on the desired port to automatically match the filter response in noise filter block. The filtered noise should be completely subtracted from the noise signal of the input speech signal and noise input signal and the error signal should contain only the original signal.
Communications in Computer and Information Science
Speech enhancement is a vital area of research, the performance of speech based human machine applications such as automatic speech recognition system, in car communication depends on the quality of speech communicated. Different methodologies have been used by various researchers to improve the quality of speech signal. In this paper an attempt is made to analyze the performance of Least Mean Square (LMS) and Recursive Least Squared (RLS) adaptive filter algorithm for speech enhancement application. The performance indices used for the evaluations is Mean Square Error (MSE), Signal to Noise Ration (SNR) and execution time. The detail analysis is done and experimentally the results are validated and certain modifications are suggested in the algorithm. The experimentation revels that LMS have fast convergence than RLS. The computational complexity of RLS is very high as compared to LMS.
Analysis of fast recursive least squares algorithms for adaptive filtering
In this paper, we present new version of numerically stable fast recursive least squares (NS-FRLS) algorithm. This new version is obtained by using some redundant formulae of the fast recursive least squares (FRLS) algorithms. Numerical stabilization is achieved by using a propagation model of first order of the numerical errors. A theoretical justification for this version is presented by formulating new conditions on the forgetting factor. An advanced comparative method is used to study the efficiency of this new version relatively to RLS algorithm by calculating their normalized square norm gain error (NGE). We provide a theoretical justification for this version by formulating new conditions on forgetting factor. It will be followed by an analytical analyze of the convergence of this version and we show, both theoretically and experimentally, their robustness. The simulation over a very long duration for a stationary signal did not reveal any tendency to divergence.
Journal of Algorithms, 2000
In this paper, a new multichannel recursive least squares (MRLS) adaptive algorithm is presented which has a number of very interesting properties. The proposed computational scheme performs adaptive filtering via the use of a finite window, where the burdening past information is dropped directly by means of a generalized inversion lemma; consequently, the proposed algorithm has excellent tracking abilities and very low misjudgment. Moreover, the scheme presented here, due to its particular structure and to the proper choice of mathematical definitions behind it, is very robust; i.e., it is less sensitive in the finite precision numerical error generation and propagation. Also, the new algorithm can be parallelized via a simple technique and its parallel form and, when executed with four processors, is faster than all the already existing schemes that perform both infinite and finite window multichannel adaptive filtering. Finally, due to the particular structure of this scheme and to the intrinsic flexibility in the choice of the window length, the proposed algorithm can act as a full substitute of the infinite window MRLS ones.
Noise Cancellation Using Adaptive Filters of Speech Signal by RLS Algorithm in Matlab
2015
In wireless communication, if the property of signal are given, fixed filter are used by us.. but when the property of signals are unknown then we require to adjust the filter, this type of filter is called adaptive filter. It is use for remove the background noise. Here, in this paper we are introducing a new method for noise cancellation through RLS algorithm in matlab. It is more efficiencally and effectively from the other method of noise cancellation. The update filter coefficient are auto considered, so this method is very fast response and find estimated error and getting the original noise. Here we are using a speech signal as a input signal that should being contained many type of noise.
A FAST Algorithm for Adaptive Filtering
2009 16th International Conference on Systems, Signals and Image Processing, 2009
In this paper, we evaluate the possibility to develop algorithms of adaptation for the applications system of acoustic echo cancellation, while maintaining equilibrium between its reduced calculation complexity and its adaptive performances. We present new algorithms versions of fast recursive least squares numerically stable (NS-FRLS). They are obtained by means of redundant formulas, available in the fast recursive least squares (FRLS) algorithms, to estimate numerical errors and to retroact them in an unspecified point of the algorithm in order to modify its numerical properties. These algorithms represent a very important load of calculation that needs to be reduced. we propose a new (M-SMFTF) algorithm for adaptive filtering with fast convergence and low complexity. It is the result of a simplified FTF type algorithm, where the adaptation gain is obtained only from the forward prediction variables and using a new recursive method to compute the likelihood variable. This algorithm presents a certain interest, for the adaptation of very long filters, like those used in the problems of echo acoustic cancellation, due to its reduced complexity, its numerical stability and its convergence in the presence of the speech signal. Its calculation complexity is of 6L (L is the finite impulse response filter length) and this is considerably reduced to (2L+4P) when we use a reduced P-size (P<<L) forward predictor.
Noise Cancellation In Speech Signal Processing Using Adaptive Algorithm
Speech has always been one of the most important carriers of information for people it becomes a challenge to maintain its high quality. 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 Squares (LMS) and Normalized Least Mean Squares (NLMS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. The Recursive Least Squares (RLS) algorithm has established itself as the "ultimate" adaptive filtering algorithm in the sense that it is the adaptive filter exhibiting the best convergence behavior. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. Recently adaptive filtering was presented, have a nice tradeoff between complexity and the convergence speed. This paper describes a new approach for noise cancellation in speech signal using the new adaptive filtering algorithm named affine projection algorithm for attenuating noise in speech signals. The simulation results demonstrate the good performance of the new algorithm in attenuating the noise
NOISE SUPPRESSION IN SPEECH SIGNALS USING ADAPTIVE ALGORITHMS
Adaptive Filtering is a widely researched topic in the present era of communications. When the received signal is continuously corrupted by noise where both the received signal and noise change continuously, then arises the need for adaptive filtering. The heart of the adaptive filter is the adaptive algorithm. This paper deals with cancellation of noise on speech signals using two algorithms-Least Mean Square (LMS) algorithm and Recursive Least Squares (RLS) algorithm with implementation in MATLAB. Comparisons of algorithms are based on SNR and tap weights of FIR filter. The algorithms chosen for implementation which provide efficient performances with less computational complexity.
IEEE Transactions on Signal Processing, 1999
The use of UD-factorization in adaptive RLS algorithms is interesting for its numeric robustness and because no square-root operations at all are involved. In this correspondence we describe a square root free fast RLS algorithm based on the UD-factorization of the autocorrelation matrix. Numerous nite precision simulations tend to indicate that this algorithm is numerically stable. The algorithm requires O(N) operations, where N is the linear lter order.