The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Review: Advances in active noise control: A survey, with emphasis on recent nonlinear techniques

In this paper, a bounded-input bounded-output (BIBO) stability condition for the recursive functional link artificial neural network (FLANN) filter, based on trigonometric expansions, is derived. This filter is considered as a member of the class of causal shift-invariant recursive nonlinear filters whose output depends linearly on the filter coefficients. As for all recursive filters, its stability should be granted or, at least, tested. The relevant conclusion we derive from the stability condition is that the recursive FLANN filter is not affected by instabilities whenever the recursive linear part of the filter is stable. This fact is in contrast with the case of recursive polynomial filters where, in general, specific limitations on the input range are required. The recursive FLANN filter is then studied in the framework of a feedforward scheme for nonlinear active noise control. The novelty of our study is due to the simultaneous consideration of a nonlinear secondary path and an acoustical feedback between the loudspeaker and the reference microphone. An output error nonlinearly Filtered-U normalized LMS adaptation algorithm, derived for the elements of the above-mentioned class of nonlinear filters, is then applied to the recursive FLANN filter. Computer simulations show that the recursive FLANN filter, in contrast to other filters, is able to simultaneously deal with the acoustical feedback and the nonlinearity in the secondary path.

On the BIBO Stability Condition of Adaptive Recursive FLANN Filters With Application to Nonlinear Active Noise Control

Conventional passive headsets often perform well in reducing high-frequency noise but perform badly to cancel the low-frequency noise. Accordingly, academic researches and companies have developed active noise-reducing headsets to cancel annoying noise at such lower frequencies using expensive digital signal processors (DSPs). This paper develops a modified feedback active noise control (ANC)algorithm to design an active noise-reducing headset with a low-cost microcontroller unit (MCU), offering a cheaper solution to noise reduction than existing audio headsets. The ways to save the computing load of a least mean square (LMS) algorithm by a microcontroller and to compensate for the output limit of speakers in a headset are also provided. Experimental results show that the proposed approach removes disturbing noise very well and outperforms the commercial headset.

Active Noise Control in Headsets by Using a Low-Cost Microcontroller

A novel nonlinear filter, which incorporates the concept of exponential sinusoidal models into nonlinear filters based on functional link networks (FLNs) has been developed in this paper. The proposed filter is designed to provide improved convergence characteristics over traditional FLN filters. The conventional trigonometric FLN may be considered as a special case of the proposed adaptive exponential FLN (AEFLN). An adaptive exponential least mean square (AELMS) algorithm has been derived and the same has been successfully applied for identification of a couple of nonlinear plants. The AEFLN-based nonlinear active noise control (ANC) system has also been designed and an adaptive exponential filtered-s least mean square (AEFsLMS) algorithm has been developed to update the weights as well as the exponential factor. Simulation study has revealed the improved noise mitigation offered by the AEFLN-based nonlinear ANC system.

Design of Adaptive Exponential Functional Link Network-Based Nonlinear Filters

This correspondence attempts to derive the exact implementation of two nonlinear active noise control (ANC) algorithms, viz. FSLMS and VFXLMS. The concept of reutilizing a part of the computations performed for the first sample while computing the next sample, for a block length of two samples, is exploited here to implement the fast and exact versions of the FSLMS and VFXLMS algorithms which are computationally efficient. Detailed computational complexity analysis for both addition and multiplication requirements is presented to show the advantage of the proposed algorithms. Appropriate simulation experiments are carried out to compare the performance equivalence of the proposed fast algorithms with their original versions.

E.P. Reddy

Papers

Fast Adaptive Algorithms for Active Control of Nonlinear Noise Processes