Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

Am Prasanna Kumar,Sm Vijaya

doi:10.52549/ijeei.v8i1.1155

Abstract

The persistent improvement of the hybrid adaptive algorithms and the swift growth of signal processing chip enhanced the performance of signal processing technique exalted mobile telecommunication systems. The proposed Artificial Neural Network Hybrid Back Propagation Adaptive Algorithm (ANNHBPAA) for mobile applications exploits relationship among the pure speech signal and noise corrupted signal in order to estimate of the noise. An adaptive linear system responds for changes in its environment as it is operating. Linear networks are gets adjusted at each time step based on new input and target vectors can find weights and biases that minimize the networks sum squared error for recent input and target vectors. Networks of this kind are quite oftenly used for error cancellation, speech signal processing and control systems. Noise in an audio signal has become major problem and hence mobile communication systems are demanding noise-free signal. In order to achieve noise-free signal various research communities have provided significant techniques. Adaptive noise cancellation (ANC) is a kind of technique which helps in estimation of un-wanted signal and removes them from corrupted signal. This paper introduces an Adaptive Filter Based Noise Cancellation System (AFNCS) that incorporates a hybrid back propagation learning for the adaptive noise cancellation in mobile applications. An extensive study has been made to explore the effects of different parameters, such as number of samples, number of filter coefficients, step size and noise level at the input on the performance of the adaptive noise cancelling system. The proposed hybrid algorithm consists all the significant features of Gradient Adaptive Lattice (GAL) and Least Mean Square (LMS) algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate (BER) parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples and input noise level. The outcomes suggest the errors are reduced significantly when the numbers of epochs are increased. Also incorporation of less hidden layers resulted in negligible computational delay along with effective utilization of memory. All the results have been obtained using computer simulations built on MATLAB platform

Highlights

In wired or wireless communication systems, noise cancellation has become a prime concern and considered an open research problem in current era of mass communication of data all over the world
The proposed Adaptive Filter Based Noise Cancellation System (AFNCS) considers training input of a vector [aij]12x 3 which is SNR values from different adaptive filters and further it assessed the above mentioned AFNCS algorithm which further results in Training output = [aij]12x1, which is a predicted/estimated SNR values for different signals of the proposed method at different dBs
It is closely observed during the numerical computation that, number of hidden layers which having a relationship with computational delay

Summary

Introduction

In wired or wireless communication systems, noise cancellation has become a prime concern and considered an open research problem in current era of mass communication of data all over the world. The mechanism and technologies are invented in recent past for noise cancellation from the noise-containing desired signal. The existence of noise in the desired signal may distort the received signal with random manner. From the researches of Qadri et al [1] and Riahi Manesh et al [2] found that some of the sources like a) non-linearity exist at RF frontend b) existence of time-varying thermal noise at receiver end and c) noise interference from adjacent environment. Earlier various algorithms were provided to identify this desired signal. The LMS algorithm considered as significant with respect to need of computational efficiency and storage ability at low convergence speed. Normalized LMS algorithm was considered at moderate sped of convergence in turn its response is sluggish for colored input signals

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Conclusion