A modified μ-weighted normalised frequency-domain LMS algorithm

In the radio observation based on the large single antenna, in order to reduce the interference of navigation signal L 2 from observation of the red-shifted H I spectral line at L-band, we used the an auxiliary channel and normalized LMS algorithm, and proposed new evaluating indicators deduced by theoretical method. Finally, we verified the suppression performance and the indicators' utility by multiple simulations and statistical method.

Among all adaptive noise cancellers, Widrow and Hoff's least mean square (LMS) algorithm has probably become the most popular because of its robustness, good tacking properties and simplicity of implementation. An important limitation of LMS algorithm is that the selection of a certain value for the step size implies compromise between speed of convergence and steady-state misadjustment, The variable step size normalized least-mean-square (VSS- NLMS) algorithm is appropriate to solve the conflicting requirement of fast convergence and low misadjustment of the LMS algorithm. To reduce noise in the output signal of the ANC, a step size for coefficient update is controlled according to the signal-to-noise ratio (SNR). This paper investigates the performance of a noise canceller with DSP processor (TI TMS320C6713) using the LMS algorithm, normalized least-mean-square (NLMS) algorithm, and VSS-NLMS algorithm. Results show the proposed combination of hardware and VSS-NLMS algorithm has not only a faster convergence rate but also lower distortion when compared with the fixed step size LMS algorithm and NLMS algorithm in real-time environments.

Simulation of active noise reduction using least mean squares (LMS) algorithm was carried out to support the development of the ANC (active noise control) prototype, which will be applied to a four-wheeled medium class gasoline-fueled Multi-Purpose Vehicle (MPV) with 2000-2400 cc engines. In this simulation, the LMS is applied to a set of synthetic and field data, to focus on the LMS being successfully applied to all field data, ignoring the delay time between errors and references microphone, and obtaining the optimal LMS step-size and order. Based on the simulation, positive result is shown by the LMS algorithm, where the best result, the superposition of the noise and antinoise signal attenuates and reaches convergence in about 20 ms. In addition, changes in the value of LMS orders do not significantly influence LMS predictions. On the other hand, the change in the step-size value is quite influential on the prediction, where the smaller the step-size, the longer the superposition time to reach convergence.

A variable step size Least Mean Square (LMS) algorithm based on the difference of the error signal (ESD-VSS) is proposed.The convergence and steady state behavior of the ESD-VSS is analyzed and the key parameters are given.Simulations are conducted and the results show that, compared with the Modified Variable Step Size (MVSS) algorithm, modified LMS algorithm and the standard LMS algorithm, the ESD-VSS provides faster convergence speed and smaller steady state misadjustment.Index Terms -LMS algorithm, variable step size, adaptive filter, MSE I . IntroductionThe LMS algorithm proposed by Widrow and Hoff has been widely used in channel equalization [1] and system identification [2], due to its robustness and simplicity.However the tradeoff between the convergence speed and the steady state misadjustment is a challenging problem.To achieve faster convergence speed and small misadjustment, a number of modified algorithms have been proposed.Ref. [3] utilized the self-correlation to control the step size, and suppress the interference of irrelevant noise effectively, but the convergence speed and the tracking capability are still expected to be improved.Ref. [4] gave a variable step size LMS algorithm using estimate signal error and thus it is of relatively faster tracking speed and smaller steady state error.However, the computational complexity of the algorithm is larger due to the tangent and exponent arithmetic, which makes it difficult to implement.

One of the most important applications of adaptive filter is Interference or noise cancellation. The objective of adaptive interference cancellation is to obtain an estimate of the interfering signal and to subtract it from the corrupted signal and hence obtain a noise free signal. The tracking performances of the LMS and NLMS algorithms are compared when the input of the adaptive filter is no stationary For this purpose, the filter uses an adaptive algorithm to change the value of the filter coefficients, so that it acquires a better approximation of the signal after each iteration. The LMS (Least Mean Square), and its variant the NLMS (Normalized LMS) are two of the adaptive algorithms widely in use. This paper presents a comparative analysis of the LMS and the NLMS in case of interference cancellation from speech signals. For each algorithm, the effects of two parameters-filter length and step size have been analyzed. Finally, the performances of the two algorithms in different cases have been compared.

This paper proposes a new variable step-size adaptive filtering algorithm based on the LMS algorithm and compares its performance with Least Mean Squares(LMS), Normalized Least Mean Squares(NLMS), Modified Sigmoid- LMS(MLMS) and Regularized NLMS(RNLMS) algorithms. The results indicate that compared to the other comparative algorithms, this algorithm can more effectively improve the signal-to-noise ratio of the filter output. Furthermore, within a wide input signal-to-noise ratio range, this algorithm can consistently enhance the filter signal-to-noise ratio output, effectively addressing the problem of inconsistent filtering effects caused by the input signal-to-noise ratio sensitivity in algorithms such as NLMS algorithms.

We describe the application of a genetic algorithm (GA) to the problem of parameter optimization for an adaptive finite impulse response (FIR) filter combining genetic algorithm (GA) and least mean square (LMS) algorithm. For system identification problem, LMS algorithm computes the filter coefficients and GA search the optimal step-size adaptively. Because step-size influences on the stability and performance, so it is necessary to apply method that can control it. The simulation results of the GA were compared to the traditional LMS algorithm. We obtained that genetic algorithm was clearly superior (in accuracy) in most cases.

A major complaint of individuals with normal hearing and hearing impairments is a reduced ability to understand speech in a noisy environment. This paper is proposed to remove the noise from the speech signal in various real time environments and to make the hearing impaired to understand the information by transforming speech in to text format. The quality of audio signal can be improved by filtering the degraded speech signal through adaptive filters. For Noise cancellation widrow & hoff’s Least Mean Square (LMS) algorithms are being used for simplicity in implementation. The LMS algorithm was simple in it's derivation and robust in a number of applications but it also have limitation in selection of a certain values such as step size and results in computational complexity. Hence to overcome all the limitations and to present an enhanced speech signal, a novel approach named VSSNDLMS algorithm is proposed. The performance of algorithms like Variable Step Size Normalised LMS , Normalised Differential LMS with proposed VSSNDLMS with different input signals are analysed in this paper . Finally, through simulation results the proposed VSSNDLMS algorithm converges fastly with minimum mean square error and it is useful in predicting the adaptive filter performance of various algorithms and the implementation indicate the improvement in quality of the speech signal and it appears to be favourable for hearing impaired. Keywords: VSSNDLMS, LMS Algorithm, Noise Cancellation, Speech Processing, Adaptive Filter

Gain optimized cosine transform domain LMS algorithm for adaptive filtering of EEG

This paper surveys recent advances related to sparse least mean square (LMS) algorithms. Since standard LMS algorithm does not take advantage of the sparsity information about the channel being estimated, various sparse LMS algorithms that are aim at outperforming standard LMS in sparse channel estimation are discussed. Sparse LMS algorithms force the solution to be sparse by introducing a sparse penalty to the standard LMS cost function. Under the reasonable conditions on the training datas and parameters, sparse LMS algorithms are shown to be mean square stable, and their mean square error performance and convergence rate are better than standard LMS algorithm. We introduce the sparse algorithms under Gaussian noises model. The simulation results presented in this work are useful in comparing sparse LMS algorithms against each other, and in comparing sparse LMS algorithms against standard LMS algorithm.

Adaptive filters, as part of digital signal systems, have been widely used, as well as in applications such as adaptive noise cancellation, adaptive beam forming, channel equalization, and system identification. However, its implementation takes a great deal and becomes a very important field in digital system world. When FPGA (Field Programmable Logic Array) grows in area and provides a lot of facilities to the designers, it becomes an important competitor in the signal processing market. In general FIR structure has been used more successfully than IIR structure in adaptive filters. However, when the adaptive FIR filter was made this required appropriate algorithm to update the filter’s coefficients. The algorithm used to update the filter coefficient is the Least Mean Square (LMS) algorithm which is known for its simplification, low computational complexity, and better performance in different running environments. When compared to other algorithms used for implementing adaptive filters the LMS algorithm is seen to perform very well in terms of the number of iterations required for convergence. This phenomenon can be achieved by a sufficient choice of bit length to represent the filter’s coefficients. This paper presents a lowcost and high performance programmable digital finite impulse response (FIR) filter. It follows the adaptive algorithm used for the development of the system. The architecture employs the computation sharing algorithm to reduce the computation complexity.

Adaptive filtering techniques have gained much popularity in the modeling of unknown system identification problem. These techniques can be classified as either iterative or direct. Iterative techniques include stochastic descent method and its improved versions in affine space. In this paper we present a comparative study of the least mean square (LMS) algorithm and some improved versions of LMS, more precisely the normalized LMS (NLMS), LMS-Newton, transform domain LMS (TDLMS) and affine projection algorithm (APA). The performance evaluation of these algorithms is carried out using adaptive system identification (ASI) model with random input signals, in which the unknown (measured) signal is assumed to be contaminated by output noise. Simulation results are recorded to compare the performance in terms of convergence speed, robustness, misalignment, and their sensitivity to the spectral properties of input signals. Main objective of this comparative study is to observe the effects of fast convergence rate of improved versions of LMS algorithms on their robustness and misalignment.

Multikernel adaptive filtering over graphs based on normalized LMS algorithm

Objectives: In case of medical emergency, an ambulance uses siren (which produces noise up to 120 dB) to alert the traffic on the road to give way. However, siren noise is troublesome to the patient lying inside. This calls for noise control inside the ambulance. Methods/Statistical Analysis: The passive noise control methods are not feasible and economically inviable so active noise control (ANC) method is applied. The ANC is electro-acoustic technique in which the noise signal is acquired and noise with opposite phase is produced and overlapped on input noise to suppress the same. The weights are updated using adaptive algorithms so as to reduce the error in ANC system. In present work, least mean square (LMS) and filtered- reference least mean square (FxLMS) algorithm based ANC systems are applied and compared for ambulance siren noise reduction. MATLAB is used for simulating the ANC systems. Findings: Both the algorithms are compared on the basis of residual noise and smoothened ensemble average square error (SEASE). From the results, it is found that both algorithms LMS and FxLMS are good in noise reduction. The noise reduction for FxLMS based ANC system is 28 dB whereas it is 25 dB for LMS based ANC system, which indicates the superiority of FxLMS algorithm over LMS algorithm. The results are obtained analytically as well as indicated by power spectral density (PSD) plots of the residual noise. The comparison of SEASE for both algorithms also indicates better performance for FxLMS algorithm. The ANC system based on FxLMS algorithm is more stable as it considers the secondary path which includes practical components such as ADC, DAC, etc. Applications/Improvements: The results indicate that FxLMS algorithm based on ANC system can be implemented on the hardware using DSP processor. It can be further used for the other applications of noise reduction such as server noise, vacuum cleaner, etc. Keywords: Active Noise Control, Ambulance Siren Noise, FxLMS Algorithm, LMS

A novel adaptive filter algorithm has been proposed that provides faster convergence than the least mean square (LMS) and normalized LMS (NLMS) algorithms. The NLMS algorithm is studied and modified so that the error sequence is used for designing the step size parameter at each iteration. A strategy to reduce the computational complexity the NLMS adaptation requires is used in the proposed algorithm. Computer simulations demonstrate that in the scenario of channel equalization, the proposed algorithm based on the structure of a transversal filter accomplishes faster start-up than the combined LMS/F algorithm recently addressed.