Evolution of Active Noise Control techniques for NICUs

A survey on active noise control in the past decade–Part II: Nonlinear systems

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

It is difficult to reduce the noise of a moving evaluation point with Active Noise Control (ANC) technique. This is because that ANC technique can control only narrow area. Therefore to expand quiet area is necessary. Although we showed that it is possible to expand quiet area against pure tone in the previous study, but it is not yet capable against random noise. In this paper, we propose the new method for expanding quiet area against random noise. Two conditions, noise reduction and noise passage conditions, for an error microphone are investigated. Under the noise reduction condition, noise is reduced at the position of error microphone. On the other hand, under noise passage condition, all control sound are cancelled each other at the position of error microphone. These conditions depend on a standard frequency. When the standard frequency is same to the noise frequency of pure tone, it makes noisier area narrow to set error microphones meeting these conditions. Accordingly, we think that to meet these conditions at many standard frequencies make noisier area narrow in random noise effectively. The control effect is shown by the numerical simulations.

With the advancement of digital signal technology, active noise control technique has gradually received more attention for the low-frequency noise in the vehicle. The active noise control apparatus and method have shortcomings in the theory and algorithm. Both acoustic devices researched on meeting the application requirements and adaptive active noise control algorithms are effective ways to improve the practicability of active noise control technique. The paper reviews various vehicle interior active noise control apparatuses in recent years. It has been found that the vehicle interior active noise control apparatuses can be classified as the extracting signal sources and the control strategy between cancelling signal and feedback signal. From review of the patents presented, it is found that there are many patents covering the active noise control method inside vehicle. However, there is a limitation in controlling speed and precision. Therefore, it is recommended that the development should be focused on the design of the control section of vehicle interior noise, which will improve the sound quality and save the space. Moreover, the cost of the new apparatus will be reduced by introducing intelligent controller. Keywords: Acoustic devices, active noise control strategy, algorithm, cancelling signal, feedback signal, vehicle interior noise.

Active noise control techniques have been studied in recent years as an available method of machinery noise reduction. Especially, there are many applications of them to ductlike structures where sound waved radiated from noise sources can be assumed to be traveling as plane waves. In most of these cases, the lengths by ducts were assumed to be infinitely long, and acoustic properties such as reflections and resonances inside the ducts have seldom been considered. In actual machines, however, the duct lengths ate often short because of the limit of machine dimensions; hence a basic study on active noise control for such short-length ducts is needed. This paper describes the modeling method of an active noise control system using a fixed FIR filter for short-length ducts. The results of simulation bclculations were compared with experimental data and good agreement was obtained.

A novel feedforward hybrid active sound quality control algorithm for both narrowband and broadband sound-profiling

The active noise control (ANC) technique attenuates acoustic noise in a flexible and effective way. Traditional ANC design aims to minimize the residual noise energy, which is indiscriminative in the frequency domain. However, human hearing perception exhibits selective sensitivity for different frequency ranges. In this paper, we aim to improve the noise attenuation performance in perceptual perspective by incorporating noise weighting into ANC design. We also introduce psychoacoustic analysis to evaluate the sound quality of the residual noise by using a predictive pleasantness model, which combines four psychoacoustic parameters: loudness, sharpness, roughness, and tonality. Simulations on synthetic random noise and realistic noise show that our method improves the sound quality and that ITU-R 468 noise weighting even performs better than A-weighting.

This paper works on the idea of integrating the acoustic scene classification technique in active noise control (ANC), whereby the major difficulty lies in the limited dataset available from an ANC system in use. A large-scale acoustic scene dataset recorded by a different acoustic device has to be considered. This leads to the research problem of acoustic scene classification by mismatched devices. Hence, an ensemble of the convolutional neural networks (CNNs) is demonstrated with a novel data augmentation method. Monaural samples are processed by the head-related transfer functions (HRTFs) of 24 azimuths to add in the artificial spatial information. Every two symmetrical azimuths are then paired together to provide a tempo-spectral feature consisting of 4 channels. 12 CNNs are trained with respective features. Finally, different ensemble strategies are carried out and their classification accuracies are compared. When the random forest is used as the meta-learner, the ensemble of CNNs achieves the best classification accuracy of 65.1% over 10 acoustic scenes.

This paper presents the theoretical development of an approach to active noise control (ANC) applicable to three-dimensional radiators. The active noise control technique, termed ANC Optimization Analysis, is based on minimizing the total radiated power by adding secondary acoustic sources on the primary noise source. ANC Optimization Analysis determines the optimum magnitude and phase at which to drive the secondary control sources in order to achieve the best possible reduction in the total radiated power from the noise source/control source combination. For example, ANC Optimization Analysis predicts a 20 dB reduction in the total power radiated from a sphere of radius α at a dimensionless wavenumber ka of 0.125, for a single control source representing 2.5 percent of the total area of the sphere. ANC Optimization Analysis is based on a boundary element formulation of the Helmholtz Integral Equation, and thus, the optimization analysis applies to a single frequency, while multiple frequencies can be treated through repeated analyses.

Active noise control (ANC) techniques have been successfully investigated and implemented for the attenuation of stationary acoustic noise. Furthermore, adaptive methods for impulsive noise have been also presented in the literature, even though the considered statistical models are well suited only for a narrow class of impulsive disturbances that can be faced in realistic scenarios. In this paper, a preliminary study addressing the design of a non-adaptive deterministic ANC system for pulse signals is developed by avoiding any statistical assumption. The theory of Wave Field Synthesis is explicitly exploited in a destructive manner, that is, generating a canceling acoustic pressure field by means of arrays of secondary sources. An ad-hoc interactive software simulator implementing the proposed approach is realized and used to virtually explore the solution spaces of some test case scenarios. Simulations’ results considering linear and planar arrays in the case of free space propagation, as well as in the case of interaction with a hard ground, are presented and compared. Furthermore, the impact of different design solutions proposed to mitigate the impairments due to terrain reflections is evaluated.

Have you ever faced a situation where you really wanted to enjoy your favorite music in a very noisy environment? To overcome this problem, you can either block the noise or increase the music level to mask the annoying sounds. This begets the question, "Is there a better solution?" In recent years, the technology industry has grown enormously, especially in the areas of smart, mobile, and wearable electronic products. Many companies are trying to gain consumers' attention by developing high-quality products equipped with advanced noise reduction. Among them, several companies have successfully used active noise control (ANC) techniques to cope with noise problems.

A reduction level against higher frequency noise is not sufficient while using ordinary active noise control (ANC) technique in general living space. In order to control broad frequency noise, active noise barrier has been proposed. Both an active and a passive control effect are expected in such active noise barriers. In the ordinary ANC, it is necessary to put a microphone (error microphone) at the point where the noise should be reduced. However, the error microphone often disturbs the movement of the person. Against this problem, virtual error microphone system (VMS) was proposed by Elliott. In the previous studies, we investigated one-channel VMS with active noise barrier, and obtained good results. In this paper, to obtain wider control area, a two-channel VMS with active noise barrier is investigated and its effectiveness is confirmed in the experiments. In the experiments, average control effect in 80cm × 80cm area is measured with various intervals of two control sound sources.

Active noise control (ANC) technique has been successfully implemented by various automotive manufacturers to interior powertrain noise cancellation and/or sound management. For the powertrain noise control, the ANC system only requires one reference signal (typically using tachometer signal) due to the fact that the powertrain response is functionally related to the engine rotational speed. Instead, the multi-reference ANC system is often required for the road noise control since the multiple-coherence between the reference accelerometer signals and the target noise must be guaranteed to yield an acceptable control performance. However, the adoption of traditional multi-reference filtered-x least mean square (FXLMS) algorithm for road noise ANC often leads to a large computational burden and slow convergence rate due to the long length of adaptive filter and a huge dynamic range of the filtered reference signal. To address these problems, road noise ANC systems configured with the time-frequency domain FXLMS (TF-FXLMS) algorithm and subband FXLMS (SFXLMS) algorithm are developed, where the adaptive algorithm either calculates the gradient estimate and filtered reference signal in the frequency domain or subband frequency domain to reduce the computational requirement and also updates the control signals in the time domain to avoid delay in the reference path. Then, a comprehensive computational complexity analysis is conducted to exhibit the computational cost for different algorithms. The effectiveness of the different algorithms will be demonstrated through an ANC system with eight accelerometers, one secondary loudspeaker, and one error microphone in the driver side to control actual road noise.

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

The method of reducing automotive intake noise can be classified by passive and active control techniques. However, passive control has a limited effect of noise reduction at low frequency range (below 500 Hz) and is limited by the space of the engine room. However, active control can overcome these passive control limitations. The active control technique mostly uses the Least-Mean-Square (LMS) algorithm, because the LMS algorithm can easily obtain the complex transfer function in real-time, particularly when the Filtered-X LMS (FXLMS) algorithm is applied to an active noise control (ANC) system. However, the convergence performance of the LMS algorithm decreases significantly when the FXLMS algorithm is applied to the active control of intake noise under rapidly accelerating driving conditions. Therefore, in this study, the Co-FXLMS algorithm was proposed to improve the control performance of the FXLMS algorithm during rapid acceleration. The Co-FXLMS algorithm is realized by using an estimate of the cross correlation between the adaptation error and the filtered input signal to control the step size. The performance of the Co-FXLMS algorithm is presented in comparison with that of the FXLMS algorithm. Experimental results show that active noise control using Co-FXLMS is effective in reducing automotive intake noise during rapid acceleration.