An Adaptive Noise Canceller Using Error Nonlinearities in the LMS Adaptation

This paper introduces an adaptive noise canceller (ANC) using a proposed variable step size least mean-square (LMS) algorithm. The step size varies between two hard limits based on a predetermined nonlinear decreasing function of signal to noise ratio (SNR) estimated at every iteration of the algorithm. The performance of the proposed algorithm is studied for different power levels of both stationary and nonstationary Gaussian noise added to the original speech. Compared with other several variable step size algorithms, computer simulations show performance superiority of the proposed algorithm in decreasing excess mean square error (EMSE) in both stationary and nonstationary noise environments. Simulations of the proposed method also show substantial improvements in decreasing misadjustment and reverberation.

Speech enhancement is performed in a wide and varied range of instruments and systems. In this paper, a novel approach to Speech Enhancement using Adaptive Empirical Mode Decomposition (SEAEMD) is presented. Spectral analysis of non-stationary signals can be performed by employing techniques such as the STFT and the Wavelet transform (WT), which use predefined basis functions. Empirical Mode Decomposition (EMD) performs very well in such environments. EMD decomposes a signal into a finite number of data-adaptive basis functions, called Intrinsic Mode Functions (IMFs). The new SEAEMD system incorporates this multi-resolution approach with adaptive noise cancellation (ANC) for effective speech enhancement on an IMF level, in stationary and non-stationary noise environments. A comparative performance study is included that compares the competitive method of conventional ANC to the robust SEAEMD system. The results demonstrate that the new system achieves significantly improved speech quality with a lower level of residual noise.

Due to its simplicity the adaptive Least Mean Square (LMS) algorithm is widely used in Code-Division Multiple access (CDMA) detectors. However its convergence speed is highly dependent on the eigen value spread of the input covariance matrix. For highly correlated inputs the LMS algorithm has a slow convergence which require long training sequences and therefore low transmission speeds. Another drawback of the LMS is the trade-off between convergence speed and steady-state error since both are controlled by the same parameter, the step-size. In order to eliminate these drawbacks, the class of Variable Step-Size LMS (VSSLMS) algorithms was introduced. In this paper, we study the behavior of some algorithms belonging to the class of VSSLMS for training based multiuser detection in a CDMA system. We show that the proposed Complementary Pair Variable Step-Size LMS algorithms highly increase the speed of convergence while reducing the trade-off between the convergence speed and the output error.

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.

This paper proposes a new application of fuzzy logic to variable step size least mean square (VS-LMS) adaptive beamforming algorithm in CDMA systems. The proposed algorithm adjusts the step size of the least mean square (LMS) by using the application of fuzzy logic in which the increase or decrease of step size depends on the fuzzy inference results of the mean square error (MSE). Computer simulation results show that the proposed algorithm has a better capacity of tracking compared with the conventional LMS algorithms and other variable step size LMS algorithms.

The step size of least mean square (LMS) algorithm is significant for its performance. To be specific, small step size can get small excess mean square error but results in slow convergence. However, large step size may cause instability. Many variable step size least mean square (VSSLMS) algorithms have been developed to enhance the control performance. In this paper, a new VSSLMS was proposed based on Kwong’s algorithm to evaluate the robustness. The approximate analysis of dynamic and steady-state performance of this developed VSSLMS algorithm was given. An active vibration control system of piezoelectric cantilever beam was established to verify the performance of the VSSLMS algorithms. By comparing with the current VSSLMS algorithms, the proposed method has better performance in active vibration control applications.

Internet of things (IoT) has made possible the sharing of data wirelessly among everyday objects and physical devices. However, distortion and noise can significantly degrade the signaling performance of IoT. Adaptive Noise Cancellation (ACN) technology plays an important role not only in IoT but also in other numerous fields such as system identification, noise cancellation, communication, signal control, etc. At present, IoT is an advance trend and need to deal with upcoming issues in a way to deal properly in enjoying this technology. Due to the presence of noise in the signal, the quality of the signal is reduced, so the quality of communication (signal) can be improved by adaptive noise (acoustic) cancellation technology. This paper aims to provide the performance analysis of adaptive algorithm and the application of adaptive noise cancellation technique. Further, use cases of three-noise cancellation filter in IoT are introduced. In wireless communication, quality of services relies on signal quality and thus noise cancellation plays a vital role in the signaling of IoT. At the end of this paper performance analysis of different ANC algorithms including least mean square (LMS), Normalized least mean square (NLMS), recursive least square (RLS) will be done based on simulation results.

Adaptive LMS (least mean squares) filter are applied to cancel harmonic noise. Simulations were done using actual noise recorded at a distribution power substation. Two implementation methods are discussed. Using the characteristic properties of the noise, a constrained filter is proposed which greatly reduces computation without reducing its effectiveness. It is shown that both constrained and unconstrained adaptive noise cancellation filters can be used effectively. For either case, significant improvement in the error rate is obtained.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A lot of work has been done recently to develop algorithms that utilize the distributed structure of anad hocwireless sensor network to estimate a certain parameter of interest. One such algorithm is called diffusion least-mean squares (DLMS). This algorithm estimates the parameter of interest using the cooperation between neighboring sensors within the network. The present work proposes an improvement on the DLMS algorithm by using a variable step-size LMS (VSSLMS) algorithm. In this work, first, the well-known variants of VSSLMS algorithms are compared with each other in order to select the most suitable algorithm which provides the best trade-off between performance and complexity. Second, the detailed convergence and steady-state analyses of the selected VSSLMS algorithm are performed. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios. Moreover, the simulation results are found to corroborate the theoretical findings very well.

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.

This paper introduces a new nonlinear filter that is used for adaptive noise canceling. The derivation and convergence properties of the filter are presented. The performance, as measured by the root mean square error between the signal and its estimate, is compared with that of the commonly used least mean square (LMS) algorithm. It is shown, through simulation, that the proposed nonlinear noise canceler has, on the average, better performance than the LMS canceler. The proposed adaptive noise canceler is based on the Pontryagin minimum principle and the method of invariant imbedding. The computational time for the proposed method is about 10% of that of the LMS, in the studied cases, which is a substantial improvement.

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.

Along with the increasing requests of the control level for power plant operation, accurate state parameters are needed for the advanced control, diagnosis and optimization algorithm. But the signal of the state parameter is obscured by all kinds of noises in thermal system and difficult to analyze. To solve this problem, a novel least-mean-square(LMS) algorithm is used for characteristic extracting in the adaptive noise cancellation (ANC) problem. An improved LMS algorithm based on Sigmoid function was presented. The simulation result shows that a superior performance of the new algorithm in stationary environment and an equivalent performance in nonstationary environment. The experiment proves the method is effective and feasible for thermal processes signal analyzing.

Magnetic Resonance Sounding (MRS) equipment for groundwater exploration has high receiving sensitivity, and it is vulnerably affected by high-level power-line harmonic when worked near the city and village. In order to effectively extract the weak signal, variable step-size LMS algorithm in frequency domain is studied to eliminate noise based on the principle of adaptive noise cancellation. Simulation results show that the algorithm can increase the signal to noise ratio more than 10dB, and the fitting error of parameters is about 10% in the condition that the frequency offset between signals and power-line harmonic is greater than 7Hz and with different SNR and water content. Therefor the variable step-size LMS algorithm in frequency domain can obtained better de-noising effect and parameter extraction accuracy.

A new approach in adjusting the step size of the least mean square (LMS) using the fuzzy logic technique is presented. It extends earlier work (Gan, 1996) by giving a complete design methodology and guidelines for developing a reliable and robust fuzzy step size LMS (FSS_LMS) algorithm. It also presents a computational study and simulation results of this newly proposed algorithm compared to other conventional variable step size LMS algorithms.