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ANNHBPAA Based Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications

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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 transceiver systems. The proposed artificial neural network hybrid back propagation adaptive algorithm for mobile applications used for noise cancellation. Adaptive noise cancellation using ANN has been implemented on audio speech signal is a new and intelligent method for real-time noise cancellation based on neural networks. Networks of this kind are quite often used for error cancellation, speech signal processing and control systems. The proposed hybrid algorithm consists all the significant features of gradient adaptive lattice and least mean square algorithms. The performance analysis of the method is performed by considering convergence complexity and bit error rate parameters along with performance analyzed with varying some parameters such as number of filter coefficients, step size, number of samples along with input noise level. The outcomes suggest the errors are reduced significantly for the number 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 hardware implementation and computer simulations built on MATLAB platform.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, ACS College of Engineering, Bengaluru, Karnataka, India

    A. M. Prasanna Kumar

  2. Department of Electronics and Communication Engineering, Rajarajeswari College of Engineering, Bengaluru, Karnataka, India

    S. M. Vijaya

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  1. A. M. Prasanna Kumar
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  2. S. M. Vijaya
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Correspondence to A. M. Prasanna Kumar.

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Kumar, A.M.P., Vijaya, S.M. ANNHBPAA Based Noise Cancellation Employing Adaptive Digital Filters for Mobile Applications. J. Inst. Eng. India Ser. B 102, 645–653 (2021). https://doi.org/10.1007/s40031-021-00593-7

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  • DOI: https://doi.org/10.1007/s40031-021-00593-7

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