Introducing Adaptive filters Based on Shadow Concept for Speech Processing

This paper presents the new approach to introducing adaptive Filter with LMS Algorithm based on Shadow concept. Which is useful for the cancellation of the noise component overlap with Speech signal in the same frequency range, but fixed LMS algorithm produces minimum convergence rate and fixed steady state error. So we presents design, implementation and performance  of adaptive FIR filter, based on  Shadow concept, which produces minimum mean square error compare to fixed LMS, and we also obtains denoised Speech signal at output, and also we propose to calculate SNR values of  Adaptive Filter with LMS algorithm with and without Shadow concept

A Novel Method for the Application of Adaptive filters for Active Noise Control System

This paper introduces one novel method for active noise control .Though use of filtered-X LMS FIR Adaptive Filter mature in the literature ,this expression illustrates the application of adaptive filters to the attenuation of acoustic noise via active noise control. The reference signal is a noisy version of the undesired sound measured near its source. We shall use a controller filter length of about 44 msec and a step size of 0.0001 for these signal statistics. The resulting algorithm converges after about 5 seconds of adaptation. We also realize adaptive algorithm using IIR filter with active noise to overcome the ability of acoustic feedback . The direct form IIR filter structure, which faces the difficulties of checking stability and of relatively slow convergence speed for noise composed of narrow band components with large power inequality. To overcome these difficulties along with using the direct form IIR filters filtered-u LMS algorithm is used

Physical configuration-based feedforward active noise control using adaptive second-order truncated Volterra filter

This paper presents a physical configuration-based feedforward active noise control scheme with an adaptive second-order truncated Volterra filter for point source cancellation in three-dimensional free-field acoustic environment. The inertial particle swarm optimization (PSO) algorithm is used as the parameter adjustment mechanism for tuning the coefficients of the adaptive Volterra filter. The first motivation of this paper is to provide a precise description of the relationship between the degree of cancellation and the physical distances between system components. The second motivation is to improve the cancellation performance in the presence of nonlinearities with the adaptive Volterra filter in light of the characteristics of sensing microphone and actuating loudspeaker. The reason for choosing the inertial PSO algorithm is that it can avoid the trap of local optima. The work thus presented makes two main contributions. The first is using the degree of cancellation as a function of the physical distances between system components to provide a quantitative analysis of system performance. The second is the application of the adaptive Volterra filter, which achieves improvements in the cancellation performance of the system under different physical configurations with a reasonable compromise with complexity. For consistency with the numerical analysis, several simulation experiments are conducted using MATLAB/Simulink

Collaborative adaptive exponential linear-in-the-parameters nonlinear filters

by Vinal Patel, Somanath Pradhan and Nithin V. Georg

Nonlinear Active Noise Control Using Adaptive Wavelet Filters

This paper deals with nonlinear active noise control using adaptive wavelet filters. The ability of wavelets in signal reconstruction and function approximation make them appealing for black box system identification. Moreover, the intrinsic similarity between wavelet filters and noise/vibration signals implies that better approximation of these signals can be achieved by employing wavelet filters. Here, a new simple structure for using in active noise control system is proposed comprises a nonlinear static mapping cascaded with an IIR filter to take care of the dynamics of the system. With this strategy, one can avoid using multi-dimensional wavelet networks and thus eliminate curse of dimensionality. The performance of the proposed ANC system is examined for typical linear/nonlinear cases. The simulation results demonstrate superior performance of this method in terms of fast convergence rate and noise attenuation as well as computational complexity reduction while avoiding curse of dimensionality

Fuzzy logic feedforward active noise control with distance ratio and acoustic feedback using Takagi–Sugeon–Kang inference

© The Author(s) 2019. Noise, as undesired sound, severely affects the quality of human life. Currently, active noise control method has demonstrated its capability in low-frequency noise cancellation and the advance in saving money and reducing weight and volume of related materials used in the passive noise control technology. The widespread configuration for active noise control technology is finite impulse response filter with filtered-x least mean squares (FxLMS) algorithm. However, the nonlinearities in the secondary path, which mainly arise from sensors, actuators and amplifiers used in the active noise control system, will cause instability and degrade the performance while using the FxLMS algorithm. In order to cope with this challenge, many new approaches have been proposed and fuzzy logic control is one of these. In this paper, a Takagi–Sugeon–Kang-type fuzzy logic control-based feedforward active noise control system with focus on the geometry configuration is introduced. In contrast to previous work, all physical paths are modelled by pure time delay transfer function and the acoustic feedback is added as part of inputs for the fuzzy logic control. Computational experiments are implemented within the Matlab/Simulink platform, and several case studies are presented with time and frequency domain analyses to demonstrate the cancellation ability of the proposed feedforward active noise control system and investigate the influence of distance ratio on the overall noise cancellation performance

DISCRETE-TIME ADAPTIVE CONTROL ALGORITHMS FOR REJECTION OF SINUSOIDAL DISTURBANCES

We present new adaptive control algorithms that address the problem of rejecting sinusoids with known frequencies that act on an unknown asymptotically stable linear time-invariant system. To achieve asymptotic disturbance rejection, adaptive control algorithms of this dissertation rely on limited or no system model information. These algorithms are developed in discrete time, meaning that the control computations use sampled-data measurements. We demonstrate the effectiveness of algorithms via analysis, numerical simulations, and experimental testings. We also present extensions to these algorithms that address systems with decentralized control architecture and systems subject to disturbances with unknown frequencies

Development of Novel Techniques to Study Nonlinear Active Noise Control

Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms