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.

/pdf/recent-advances-on-active-noise-control-open-issues-and-1wg2ydx57i.pdf

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

Recently, several data extraction methods from data embedding printing images by using cellular phones with a camera have been proposed. In these techniques, consideration for geometrical deformation and lens distortion in the data extraction process is essential. This paper proposes new correction algorithm based on line based correction (LBR) and projective transformation to the scanning of the printing images for reduction of the geometrical deformation by the camera. This method can be applied to both of barrel distortion and pincushion one. To increase the amount of embedding data, a new data embedding method in which the codes for data embedding are changed according to the embedding data is also proposed. From experimental results, effectiveness of the proposed method for the color image is shown.

Implementation of data embedding to printing images for information retrieving by cellular phones considering lens distortion

A Method of Data Embedding and Extracting for Information Retrieval Considering Mobile Devices

Design of both a suitable window shape and appropriate weights in weighted median filters is one of important problems. Hitherto, unsupervised design methods of the filters by using Simulated Annealing (SA) or Genetic Algorithm (GA) have been proposed for texture images corrupted by impulse noise. These techniques estimate the optimal window shape and the optimal filter weights separately, and they have been shown to perform as well as a supervised method. In this paper, we propose a new approach which optimize both the window shape and the weight at the same time to design more sophisticated filters. We apply GA and adopt Rank-Ordered Logarithmic Difference (ROLD) statistics as objective function to design a weighted median filter. Through experiments, it was shown that our new approach outperformed compared to conventional methods.

An improvement of unsupervised design method for weighted median filters using GA

For complicated noise fields, a multiple channel active noise control (ANC) system with several secondary sources, error sensors, and reference sensors is required. A multiple error filtered-X (MEFX) algorithm is developed for an adaptive algorithm in the multiple channel ANC system. This algorithm requires a previous estimation of the error paths. However, the characteristics of the error paths should be assumed to change during the operation of the system. This paper proposes a control algorithm for the multiple channel ANC system without the previous estimation of the error paths. This algorithm becomes an extension of the simultaneous equations method proposed for the single channel ANC system. In the proposed method, the good performances for noise cancellation and tracking of the changes in the error paths are achieved.

Multiple channel active noise control system based on simultaneous equations methods

In this paper, we propose a method for repeatedly updating the coefficients of the feedback control filter used for canceling a feedback path from a loudspeaker to a noise detection microphone. The coefficients are usually estimated by feeding a sequence of extra noise to the loudspeaker. However, feeding such extra noise under active noise control is undesirable. The proposed method estimates the coefficients without feeding the extra noise. Concretely the proposed method provides two independent equations using the estimation errors involved in the coefficients of the noise control filter and then estimates the coefficients by solving those equations. One problem is that the feedback component is extremely small in comparison with the primary noise incident on the noise detection microphone. In this case, estimation of the coefficients is difficult. In this work. we solves this problem by estimating first the primary noise and then the feedback path.

Mitsuji Muneyasu

Papers

Implementation of data embedding to printing images for information retrieving by cellular phones considering lens distortion

A Method of Data Embedding and Extracting for Information Retrieval Considering Mobile Devices

An improvement of unsupervised design method for weighted median filters using GA

Multiple channel active noise control system based on simultaneous equations methods

Frequency domain method to estimate the coefficients of feedback control filter for active noise control systems