NDLI: Advanced adaptive mechanisms for active noise control: A technical comparison

Content Provider	IEEE Xplore Digital Library
Author	Mohapatra, S. Kar, A. Chandra, M.
Copyright Year	2014
Description	Author affiliation: Electron. & Commun. Eng. Dept., BIT Mesra, Ranchi, India (Chandra, M.) \|\| Electr. & Electron. Eng. Dept., BITS Pilani, Pilani, India (Mohapatra, S.) \|\| Electron. & Telecommun. Eng. Dept., IIIT, Bhubaneswar, India (Kar, A.)
Abstract	Noise is a growing problem in our society. It creates unwanted disturbance and needs to be dealt with. Active Noise Control (ANC) is the most effective way today, to cancel noise. ANC has a wide range of applications in common commercial products, various industrial applications and other machinery. ANC takes the use of adaptive filters which are governed by various adaptive algorithms. Adaptive algorithms work on the principle of optimisation. Years of work has led to the development of more efficient algorithms for various conditions which drive the filter to give a better performance. A review of the existing adaptive mechanisms is essential for future research as we need to know the demerits of all the existing adaptive mechanisms in ANC. In this review paper, we have discussed and compared the previous work done on the various algorithms related to ANC. The merits, demerits and practical effectiveness of all the algorithms have been discussed. Basic algorithms like the Least Mean Square (LMS) and Recursive Least Square as well as their variant versions like Normalised LMS, Variable Step Size LMS and Filtered-X LMS have been considered. Numerous works on ANC have been analysed. Finally, conclusions have been drawn with the help of a tabular comparison of the algorithms.
Starting Page	370
Ending Page	375
File Size	238355
Page Count	6
File Format	PDF
e-ISBN	9781479950973
DOI	10.1109/MedCom.2014.7006035
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-07
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Least squares approximations LMS Heuristic algorithms Noise Adaptive filters Adaptive algorithms Active Noise Control Mean Square Error Filtering algorithms Recursive Least Square Convergence Adaptive Filter
Content Type	Text
Resource Type	Article

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