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.

/pdf/neural-edge-enhancer-for-supervised-edge-enhancement-from-1n8x1nx2jy.pdf

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

An Unsupervised Design Method for Weighted Median Filters Using Genetic Algorithm

In this paper, we propose a sound field creation system based on the simultaneous equations method used for active noise control systems. Conventional studies on such sound field creation mainly focus on the transaural system reproducing recorded signals at listening points. We differently purpose creating the same sound field as a target that. In the proposed system, inverse filters forming the sound field are derived by using the simultaneous equation method, which is characterized by auxiliary filters estimating the error between the created and the target sounds. The auxiliary filters can be also used for detecting the lack of taps of the inverse filters and the lack of delays given to the target sound field. The latter lack not only increases the error but also generates echoes preceding the created sound, which annoys listeners. The performance of the proposed system and the lack detection are verified by computer simulations.

Sound field creation based on simultaneous equations method

This paper proposes a novel edge-preserving adaptive weighted mean filter using fuzzy clustering. An input vector in the filter mask is classified according to predefined clusters and the membership values corresponding to all clusters are obtained. The filter output is given by the weighted sum of the membership values with the inner products of the input vector with weight vectors according to the clusters. The proposed filter can reduce mixed noises with preserving edges satisfactory, because a fuzzy clustering flexibly classifies ambiguous local image information and adaptively controls filter weights.

An improvement of an adaptive weighted mean filter using fuzzy clustering

This paper proposes a microphone array system separating speech signals, based on a different principle from independent component analysis (ICA). This system applies linear prediction error filters to microphone outputs, and using the prediction errors, adjusts the coefficients of adaptive filters. In this case, only the prediction errors satisfying the law of causality become available for the adjustment; consequently, this system can steer a null toward a direction satisfying it. The permutation problem discussed in ICA can be thereby avoided. In practical use, the speech signals are required to be separated as quickly as possible. This paper proposes applying a frequency domain technique to the adjustment of the coefficients of adaptive filters. The application of the frequency domain technique is expected to improve the adjustment speed. Computer simulations show that the adjustment time can be reduced to about two second and that the separation effect of 30 dB can be obtained.

Speech separation microphone array based on law of causality and frequency domain processing

A new technique for designing adaptive state-space digital filters using stable filter structures is developed. First, the coefficient sensitivities are related to intermediate transfer functions in order to generate gradient signals. Next, the LMS algorithm is applied to construct adaptive state-space digital filters with new systems to generate gradient signals. To illustrate the validity of the proposed technique, a numerical example is given. In the example, the comparison between the proposed and conventional adaptive filters is presented

Mitsuji Muneyasu

Papers

An Unsupervised Design Method for Weighted Median Filters Using Genetic Algorithm

Sound field creation based on simultaneous equations method

An improvement of an adaptive weighted mean filter using fuzzy clustering

Speech separation microphone array based on law of causality and frequency domain processing

Design of adaptive state-space digital filters using stable filter structures