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 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 low 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. This system also can compensate the separated speech signals by using adaptive filters, and finally, can provide high quality speech signals. This paper moreover verifies the performance of the proposed system by using speech signal data measured in an ordinary room. This result shows that the proposed system works well even in reverberation environment.

Speech separation based on law of causality

This paper presents a multiple channel active noise control system using the simultaneous equations method based on the different principle from the filtered-x algorithm. The simultaneous equations method can automatically recover the noise reduction effect degraded by acoustic path changes. In this paper, we apply the method to multiple channel active noise control systems and verify the performance of the systems by computer simulations.

Multiple channel active noise control system using simultaneous equations method

In this paper, we propose a video retrieval method employing reranking and relevance feedback with tag-based similarity. In the proposed method, we apply relevance feedback to find more relevant videos. Furthermore, we focus on the fact that multiple tags are used to represent video content. Specifically, by vectorizing multiple tags associated with videos based on the word2vec algorithm, we calculate a new tag feature vector. We then formulate the reranking process with relevance feedback as an optimization problem whose regularization terms measure the relevance from both the video and tag features. The integration of the two schemes results in mutual benefits and improves the performance of video retrieval.

Video Retrieval by Reranking and Relevance Feedback with Tag-Based Similarity

This paper proposes a method for shortening the distance from a noise detection microphone to a loudspeaker forming the feedforward type of active noise control (ANC) system. In the ANC system, the primary noise detected by the noise detection microphone and the secondary noise provided by the loudspeaker is required simultaneously arriving at another microphone, called error microphone, monitoring the effect of the active noise control. However, the reverberation involved in the secondary path from the loudspeaker to the error microphone increase the secondary noise components arriving later than the primary noise. The late components are not only invalid for cancelling the primary noise but also impede the cancellation. This paper hence proposes a method reducing the components by forming the noise control filter synthesizing the secondary noise with the cascade connection of an infinite and a finite impulse response filters. The distance can be thereby shortened.

Active noise control system using cascade connection of finite and infinite impulse response filters as noise control filter

In this paper, we propose a method capable of shortening the distance from a noise detection microphone to a loudspeaker, which is one of important issues in the field of active noise control (ANC) In the ANC system, the secondary noise provided by the loudspeaker is required arriving at an error microphone simultaneously with the primary noise to be cancelled However, the reverberation involved in the secondary path from the loudspeaker to the error microphone increases the secondary noise components arriving later than the primary noise The late components are not only invalid for canceling the primary noise but also impede the cancellation To reduce the late components, the distance between the noise detection microphone and the loud speaker is generally extended The proposed method differently reduces the late components by forming the noise control filter, which produces the secondary noise, with the cascade connection of a non-recursive and a recursive filters The distance can be thus shortened On the other hand, the recursive filter is required to work stably The proposed method guarantees the stable work by forming the recursive filter with the lattice filter whose coefficients are restricted to less than unity

Mitsuji Muneyasu

Papers

Speech separation based on law of causality

Multiple channel active noise control system using simultaneous equations method

Video Retrieval by Reranking and Relevance Feedback with Tag-Based Similarity

Active noise control system using cascade connection of finite and infinite impulse response filters as noise control filter

Application of Cascade Connection of Recursive and Non-recursive Filters to Active Noise Control System Using Simultaneous Equations Method