An Optimized Structure Filtered-x Least Mean Square Algorithm for Acoustic Noise Suppression in Wireless Networks

Chapter
Part of the Signals and Communication Technology book series (SCT)

Abstract

In order to get distortionless signal, the noise that presents in original intended signal must be canceled. Various methods have been proposed to reduce acoustic noise which includes noise barriers, noise-absorbing circuits, and filters, whereas one of the effective ways of noise suppression is the use of adaptive filters by which continuous weight adaptation is done till the noise is minimized. Some of the basic noise reduction adaptive algorithms include least mean square (LMS) algorithm, normalized LMS algorithm, and recursive least square algorithm. The performance of the LMS algorithm is degraded in active noise control due to the presence of a transfer function in the auxiliary path following the adaptive filter. It degrades the rate of convergence and increases the residual power. To ensure convergence of the algorithm, the filtered-x least mean square (FX-LMS) algorithm finds its application where input to the error correlator is filtered by a copy of the auxiliary error path transfer function, whereas the FX-LMS results in very slow convergence performance in case of high tap length requirement for acoustic noise cancelation applications. In this paper, an improved pseudo-fractional tap length selection algorithm is proposed in context with the FX-LMS algorithm to find out the optimum structure of the acoustic noise canceler which best balances the complexity and steady-state performance.

Keywords

Adaptive algorithm Tap length Active noise control Least mean square Mean square error FX-LMS 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Electronics and Telecommunication EngineeringIIITBhubaneswarIndia
  2. 2.Department of Electronics and Communication EngineeringBIT, MesraRanchiIndia

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