A Decisive Filtering Selection Approach For Improved Performance Active Noise Cancellation System

Abstract

In this work we present a filtering selection approach for efficient ANC system. Active noise cancellation (ANC) has wide application in next generation human machine interaction to automobile Heating Ventilating and Air Conditioning (HVAC) devices. We compare conventional adaptive filters algorithms LMS, NLMS, VSLMS, VSNLMS, VSLSMS for a predefined input sound file, where various algorithms run and result in standard output and better performance. The wiener filter based on least means squared (LMS) algorithm family is most sought after solution of ANC. This family includes LMS, NLMS, VSLMS, VSNLMS, VFXLMS, FX- sLMS and many more. Some of these are nonlinear algorithm, which provides better solution for nonlinear noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit nonlinearities themselves. The nonlinear transfer function create worse situation. This is a task which is some sort of a prediction of suitable solution to the problems. The Radial Basis Function of Neural Networks (RBF NN) has been known to be suitable for nonlinear function approximation (1). The classical approach to RBF implementation is to fix the number of hidden neurons based on some property of the input data, and estimate the weights connecting the hidden and output neurons using linear least square method. So an efficient novel decisive approach for better performing ANC algorithms has been proposed. I. OVERVIEW Acoustic Noise Cancellation is a method for reducing undesired noise. It is achieved by introducing a canceling anti-noise wave through secondary sources. These secondary sources are interconnected through an electronic system using a specific signal processing algorithm for the particular cancellation scheme. Noise cancellation makes use of the notion of destructive interference. When two sinusoidal waves are superimposed, the resultant waveform depends on the frequency amplitude and relative phase of the two waves. If the original wave and the inverse of the original wave encounter at a junction at the same time, total cancellation occurs. The challenges are to identify the original signal and generate the inverse noise cancellation on inverse wave without delay in all directions where noises interact and superimpose. We meet in our everyday life. Echo phenomena are interesting and entertaining, but their presences in communication networks are undesirable and represent a serious problem. Echo is delayed and degraded version of original signal which travels back to its source after several reflections or because of some other reason. Nature of echo signal can be either acoustic or electrical, and in order to reduce its undesired effect we employ echo cancellers. Design of echo cancellers requires an application of adaptive filter theory. Echo cancellers must work within specific time limits so adaptive algorithms must provide fast convergence of filter parameters. Weve been applying echo cancellers successfully for many years, but we always tend to improve them and increase their efficiency. The wiener filter based least means squared (LMS) algorithm family is most sought after solution of ANC. This family includes LMS , Fx-LMS, VFx-LMS, FsLMS and many more . Then there are Kalman filter algorithms which are basically based on recursive least square algorithm. Some of these are nonlinear algorithm, which provides better solution for non linear noisy environment. The components of the ANC systems like microphones and loudspeaker exhibit nonlinearities themselves. The non linear transfer function of primary and secondary path may itself create worse situation. II. BACKGROUND LITERATURE REVIEW