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Top1. Introduction
Extraction of high resolution information signals is important in all practical applications. In our modern mechanized world, research on noise control has become increasingly important. The need to cancel or filter noise is not only a matter of human comfort, but will also reduce the stress imposed by vibrations on mechanical structures. Generally, filtering techniques are classified as non-adaptive and adaptive. In the case of non-adaptive filtering, filter characteristics are fixed or constant irrespective of input noise, i.e., treatment is similar for any type of noise. These types of filters are acceptable if a noise signal is stationery. In practical cases, the noises generated by various types of machinery are not stationery, and their magnitude, frequency, phase and intensity vary instantaneously. In such cases, non-adaptive filters, whose coefficients are constant, are unable to control noise; therefore, adaptive filters should be utilized. These filters are capable of changing their filter coefficients; perform filtering depending up on the input signal (hence the designation adaptive). The concept of noise reduction in mechanical machinery is known as active noise control (ANC), and adaptive filter plays a key role. ANC use the phenomenon of wave interference; when two waves with the same amplitude and frequency, but phase-reversed, travel in the same direction, they neutralize each other to destructive interference. The resulting sound is null as, the sound energy is transformed into heat. In typical ANC, a reference signal is generated and the adaptive filter adjusts the amplitude and phase of the reference signal to minimize the noise signal.
The Least Mean Square (LMS) algorithm is a basic adaptive algorithm that has been extensively used in many applications due to its simplicity and robustness. Adaptive filters are normally defined for problems such as electrical noise cancelling where the filter output is an estimate of a desired signal. In control applications, however, the adaptive filter works as a regulator, controlling a dynamic system containing actuators, amplifiers etc. The estimate (anti-vibrations or anti-sound) in this case can thus be seen as the output signal from a dynamic system, i.e. a forward path. Since there is a dynamic system between the filter output and the estimate, the selection of adaptive filter algorithms must be made with care. A conventional adaptive algorithm such as the LMS algorithm is likely to be unstable in this application due to the phase shift (delay) introduced by the forward path. The well-known filtered-x LMS (FXLMS) algorithm is, however, an adaptive filter algorithm that is suitable for active noise control applications. It is developed from the LMS algorithm, where a model of the dynamic system between the filter output and the estimate, i.e. the forward path is introduced between the input signal and the algorithm for the adaptation of the coefficient vector. Because of this feature, the FXLMS algorithm is extensively used in active noise control.