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

FIRE operators for image processing

We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images.

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Neural edge enhancer for supervised edge enhancement from noisy images

Active noise control (ANC) has been widely applied in industry to reduce environmental noise and equipment vibrations. Most available control algorithms require the identification of the secondary path, which increases the control system complexity, contributes to an increased residual noise power, and can even cause the control system to fail if the identified secondary path is not sufficiently close to the actual path. In this paper, based on the geometric analysis and the strict positive real (SPR) property of the filtered-x LMS algorithm, we introduce a new ANC algorithm suitable for single-tone noises as well as some specific narrowband noises that does not require the identification of the secondary path, though its convergence can be very slow in some special cases. We are able to extend the developed ANC algorithm to the case of active control of broadband noises through our use of a subband implementation of the ANC algorithm. Compared to other available control algorithms that do not require secondary path identification, our developed method is simple to implement, yields good performance, and converges quickly. Simulation results confirm the effectiveness of our proposed algorithm

A New Active Noise Control Algorithm That Requires No Secondary Path Identification Based on the SPR Property

The paper developed a reasonable and practical method for identifying the useful information from the signal that has been contaminated by noise, so that to provide a feasible tool for vibration analysis. A new concept namely the Singular Entropy (SE) was proposed based on the singular value decomposition technique. With the aid of the SE, a series of investigations were done for discovering the distribution characteristics of noise contaminated and pure signals, and consequently an advanced noise reduction method was developed. The experiments showed that the proposed method was not only applied for dealing with the stationary signals but also applied for dealing with the non-stationary signals.

https://msvlab.hre.ntou.edu.tw/NDT-Yang-2003.pdf

Development of an advanced noise reduction method for vibration analysis based on singular value decomposition

This paper treats the coefficient sensitivity of two-dimensional (2-D) separable-denominator digital filters using the Roesser local state-space model. Two techniques suitable for 2-D separable-denominator digital filters are developed for synthesizing the filter structure with low sensitivity, one free from 12 scaling constraints on the state variables, and the other under the scaling constraints. In the paper, it is clarified that the filter structures with low sensitivity can be easily derived from the balanced realization. Finally, an example is given to illustrate the utility of the proposed techniques.

Synthesis of 2-D separable-denominator digital filters with low sensitivity

In an active noise control system that uses the Filtered-x method as an adaptive algorithm, the impulse response on the error path is observed and the result is assigned as a coefficient of an error path filter before the system is started. Such an impulse response can obviously change after the system is started. The change can increase the difference between the assigned coefficient and the inherent coefficient of the error path filter, and thus render the operation of noise control unstable. This paper proposes a noise control filter coefficient renewal method that does not require the calculation of an error path filter coefficient by focusing on the fact that, when a set of two different coefficients is assigned to a noise control filter, the system consisting of components from a noise detection microphone to an error detection microphone establishes two independent equations whose variables are impulse responses for the feedforward path and the error path system. The coefficient of the noise control filer can be renewed by solving the simultaneous equations by iteration. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(12): 101–108, 2002; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecjc.1132

Active noise control system using the simultaneous equation method without the estimation of error path filter coefficients

A model for texture description, called "Primitive and Point Configuration (PPC) texture model," and an estimation method of the primitive, which is an elementary object for configuring a texture, are proposed in this paper. The PPC texture model regards that a texture is composed by arranging grains that are derived from one or a few primitives by some modification. The primitive shape is estimated by the principle that the primitive resembling the grains best should be the optimal estimation. This estimation is achieved by finding the structuring element that minimizes the integral of the size distribution function of a target texture.

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Primitive and point configuration texture model and primitive estimation using mathematical morphology

Application of opening, which is a basic operation of mathematical morphology, often yields undesired results like the occurrence of artifacts. This problem can be avoided by using a gray scale structuring element resembling the objects in the target image. Since a texture image is composed of an arrangement of similar small objects, it is possible to derive such a gray scale structuring element by estimating one representative gray scale object from those arranged in the texture image. In this paper, an estimation method of the optimal gray scale structuring element using "primitive, grain, and point configuration (PGPC)" texture model is proposed.

Optimization of gray scale morphological opening for noise removal in texture images

This paper proposes a novel two-dimensional (2-D) adaptive filter by applying a 1-D recursive least-squares (RLS) algorithm along both horizontal and vertical directions. The relation of the proposed algorithm to a usual 2-D RLS algorithm are investigated. A method that employs a priori estimation error is also considered to accelerate the convergent rate of the algorithm. The proposed filter has a good performance in nonstationary case, and the accuracy of convergence is better than in the existing 2-D least mean square (LMS) adaptive filters. The amount of computations required for the proposed algorithm are relatively small. Finally, an example is given to illustrate the utility of the proposed filter.

Mitsuji Muneyasu

Papers

Synthesis of 2-D separable-denominator digital filters with low sensitivity

Active noise control system using the simultaneous equation method without the estimation of error path filter coefficients

Primitive and point configuration texture model and primitive estimation using mathematical morphology

Optimization of gray scale morphological opening for noise removal in texture images

A novel 2-D adaptive filter based on the 1-D RLS algorithm