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Journal ArticleDOI

Active noise control

TLDR
The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized.
Abstract
Active noise control exploits the long wavelengths associated with low frequency sound. It works on the principle of destructive interference between the sound fields generated by the original primary sound source and that due to other secondary sources, acoustic outputs of which can be controlled. The acoustic objectives of different active noise control systems and the electrical control methodologies that are used to achieve these objectives are examined. The importance of having a clear understanding of the principles behind both the acoustics and the electrical control in order to appreciate the advantages and limitations of active noise control is emphasized. A brief discussion of the physical basis of active sound control that concentrates on three-dimensional sound fields is presented. >

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Citations
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Journal ArticleDOI

Active noise control: a tutorial review

TL;DR: The basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control, then modified for narrow-bandFeedforward and adaptive feedback control, which are expanded to multiple-channel cases.
Journal ArticleDOI

Genetic algorithms and their applications

TL;DR: The genetic algorithm is introduced as an emerging optimization algorithm for signal processing and a number of applications, such as IIR adaptive filtering, time delay estimation, active noise control, and speech processing, that are being successfully implemented are described.
Journal ArticleDOI

The evolution of multiferroics

TL;DR: In this article, a review of multiferroic thin-film heterostructures, device architectures, and domain and interface effects is presented. But the focus of the field is now shifting into neighbouring research areas, as discussed in this review.
Journal ArticleDOI

Adaptive Volterra filters for active control of nonlinear noise processes

TL;DR: Numerical simulation results show that the developed VFXLMS algorithm achieves performance improvement over the standard filtered-X LMS algorithm for the following two situations: the reference noise is a nonlinear noise process, and at the same time, the secondary path estimate is of nonminimum phase; and the primary path exhibits the nonlinear behavior.
BookDOI

Handbook of Noise and Vibration Control.

TL;DR: The Handbook of Noise and Vibration Control by Malcolm J. Crocker as discussed by the authors, New Jersey, 2007 1584 pp. Price: $195.00 (hardcover) ISBN: 0471395994
References
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Book

Adaptive filtering prediction and control

TL;DR: This unified survey focuses on linear discrete-time systems and explores the natural extensions to nonlinear systems and summarizes the theoretical and practical aspects of a large class of adaptive algorithms.
Journal ArticleDOI

Adaptive noise cancelling: Principles and applications

TL;DR: It is shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution.
Journal ArticleDOI

A multiple error LMS algorithm and its application to the active control of sound and vibration

TL;DR: An algorithm is presented to adapt the coefficients of an array of FIR filters, whose outputs are linearly coupled to another array of error detection points, so that the sum of all the mean square error signals is minimized.
Journal ArticleDOI

Inverse filtering of room acoustics

TL;DR: In this article, a novel method is proposed for realizing exact inverse filtering of acoustic impulse responses in room, based on the principle called the multiple-input/output inverse theorem (MINT).
Proceedings ArticleDOI

Adaptive inverse control

TL;DR: In this paper, a method for adaptive control of plant dynamics and for controlling plant disturbance for unknown linear plants using neural networks is described. But this method is not suitable for control of nonlinear plants.