Active Noise Control Approach Research Articles

On Time–Frequency Domain Flexible Structure of Delayless Partitioned Block Adaptive Filtering Approach for Active Noise Control

On Time–Frequency Domain Flexible Structure of Delayless Partitioned Block Adaptive Filtering Approach for Active Noise Control

Experimental Evaluation of Adaptive Step-Size Based FxLMS Approach for Active Noise Control in Magnetic Resonance Imaging Systems

Magnetic Resonance Imaging (MRI) scanners emit up to 135 decibels of acoustic noise, which is a major source of discomfort for patients and personnel evaluating them during routine medical scans, necessitating the development of a method to reduce the acoustic noise generated during MRI testing. The goal of this study is to propose a frequency-domain Active Noise Control (ANC) method for acoustic noise reduction in MRI and to demonstrate its ANC effectiveness on an experimental MRI scanner model specifically built for this purpose. In comparison to the standard Least Mean Square (LMS) algorithm, we used the Filtered-x Least Mean Square (FxLMS) approach with an adaptive variable step-size approach to adjust the filter coefficients dynamically, which considerably enhances the ANC system's convergence and reduces acoustic noise. The simulation results obtained from the MATLAB Simulink model on a pre-recorded 30-sec MRI noise signal represented by the step-size variation over time, error and noise convergence plots reveal that the adaptive step-size FxLMS (ASFxLMS) technique increases noise and error convergence rate significantly more than existing ANC algorithms to facilitate its use during MRI scans. Experimental results with our functional MRI (fMRI) testbed show approximately 25-dB overall noise reduction relative to the noise levels without ANC.

Deep MCANC: A deep learning approach to multi-channel active noise control

Traditional multi-channel active noise control (MCANC) is based on adaptive filtering and usually uses a separate control unit for each channel. This paper introduces a deep learning based approach for multi-channel active noise control (ANC). The proposed approach, called deep MCANC, encodes optimal control parameters corresponding to different noises and environments, and jointly computes the multiple canceling signals to cancel or attenuate the primary noises captured at error microphones. A convolutional recurrent network (CRN) is employed for complex spectral mapping where the summated power of error signals is used as the loss function for CRN training. Deep MCANC is a fixed-parameter ANC approach and large-scale multi-condition training is employed to achieve robustness against a variety of noises. We explore the performance of deep MCANC with different setups and investigate the impact of factors such as the number of loudspeakers and microphones, and the position of a secondary source, on ANC performance. Experimental results show that deep MCANC is effective for wideband noise reduction and generalizes well to untrained noises. Moreover, the proposed approach is robust against variations in reference signals and works well in the presence of nonlinear distortions.

Adaptive active noise feedforward compensation for exhaust ducts using a FIR Youla parametrization

The adaptive feedforward control strategy can effectively attenuate the low-frequency broad-band noise with unknown and variable characteristics. However, most feedforward active noise control systems have the internal acoustic feedback coupling between the secondary noise source and the measurement of the image of the reference noise, which may lead to instability of the system. In addition, there are potential fields of applications in which the broad-band noise source may be mixed with the narrow-band noise components and the delay of the secondary path may be larger than the delay of the primary path. In order to deal with these limitations, in this paper an adaptive Youla(Q) parameterized feedforward active noise control system is introduced. Firstly, a central controller is designed to stabilize the internal acoustic feedback loop. Then the central controller will be augmented into a Youla(Q) parameterized FIR-type adaptive controller with the recursive least squares (RLS) adaptive algorithm, and the Q parameter can be adjusted online to the desired value to attenuate the unknown and variable noise. Finally, the proposed adaptive Youla(Q) parameterized feedforward active noise control approach is successfully applied to the noise attenuation of a “U”-shaped industrial exhaust duct.

Adaptive-gain algorithm on the fixed filters applied for active noise control headphone

Active noise control (ANC) headphones are commonly used to reduce annoyed noise around users’ ears. Most commercial ANC headphones utilize specific filters with pre-trained coefficients due to their fast response and robustness. However, when dealing with primary noises coming from different directions, their noise reduction performance suffers significantly. Hence, we proposed an adaptive gain (AG) algorithm on the fixed filters with multi-reference method, in which the control signal is formed by adaptively weighting and summing the output signals of multiple fixed filters that have been pre-trained from primary noise with different directions of arrival. By combining the adaptive algorithm with the fixed filter approach, the proposed algorithm outperforms the conventional fixed ANC approach in noise reduction performance. This paper provides a theoretical analysis of the proposed algorithm’s step-size bound and time constant, demonstrating its robustness and fast convergence behavior. Furthermore, comparative simulations and real-time experiments with commercial ANC headphone and other adaptive algorithms show its efficacy in dealing with multiple noise sources in the actual scenario.

A family of logarithmic hyperbolic cosine spline nonlinear adaptive filters

Spline nonlinear adaptive filters are well known for their ability to efficiently model nonlinear systems while having low computational complexity. However, the performance of traditional spline adaptive filter degrades in the presence of impulsive disturbances. For improving the performance of spline adaptive filters in impulsive noise scenarios, a logarithmic hyperbolic cosine spline adaptive filter is proposed in this paper. To improve the convergence rate while maintaining low steady-state error, a variable parameter approach for the logarithmic hyperbolic cosine spline adaptive filter is also proposed. In addition, a robust nonlinear active noise control approach is also designed based on the proposed logarithmic hyperbolic cosine spline adaptive filter. The computational complexity of the proposed algorithms is studied and two approximate versions of the logarithmic hyperbolic cosine spline adaptive filter are proposed to decrease computational cost without degrading performance. Bound on learning rates are also derived to ensure the stability of the proposed adaptive systems. The simulation studies conducted, demonstrate the enhanced performance achieved by the proposed algorithms.

A combined bilateral and binaural active noise control algorithm for closed-back headphones

Commercial noise-cancelation headphones are based on active noise control (ANC) algorithm. However, all existing ANC headphones are based on bilateral ANC approach, where two independent monaural ANC systems are used respectively for the left and the right ear cups. The performance of bilateral ANC approach strictly depends on the number of noise sources, direction of noise sources and noise types. Its performance decreases when the number of noise sources increases. Human’s hearing system is binaural in nature and noise control can take advantage of binaural processing to further enhance noise reduction compared to conventional bilateral ANC headphones. In this paper, we first propose a binaural ANC algorithm to evaluate its performance over the bilateral ANC algorithm and subsequently, modify into a combined bilateral-binaural ANC (CBBANC) algorithm for headphones in order to improve noise reduction performance for different cases when there are more than one noise sources and all noise sources are situated at different locations and for diffuse field noise. Experimentation results show that the combined binaural-bilateral ANC has better performance in all our tests compared to the conventional bilateral ANC headphones.

A review of research on active noise control near human ear in complex sound field

Local control of sound around human ears in complex acoustic environments is important for both active noise control and sound reproduction. Two typical active noise control approaches for this objective are active headrest systems and virtual sound barrier systems. In this paper, the history and the present status for the active headrest systems and virtual sound barrier systems are briefed first, then the theoretical principles, the design methods and the applications of these approaches are reviewed. Their advantages and limitations are discussed, and finally, the currently existing problems and future research directions are presented. The feasibility of these approaches to generating a quiet zone near a human ear has been verified by the theoretical research, numerical simulations and experiments. The active headrest systems require less control sources and are simpler for implementation; however, they suffer the problem of small-sized quiet zones. This results in the restrictions on the head movement since the error sensor needs to be close to the human ear to obtain better noise reduction performance. Based on the virtual sensor technology, a physical error sensor can be placed farther away from the human head, and create the quiet zone at the virtual sensor position near the human ear. Moreover, combined with the virtual sensor technology and the head-tracking technology, an active headrest system can generate a moving zone of quiet following the head movement, and the noise reduction can be achieved in a middle-to-high frequency range. A virtual sound barrier system reduces the sound pressure inside a volume, through controlling the sound pressure and normal gradient on the boundary of the volume. Two main design methods are the expansion method of the primary sound field which is suitable for steady primary sound fields, and the least mean square method which is applicable to time-varying primary sound fields. It can generate larger quiet zone at the cost of more control sources, more complexity and high cost. Optimizing cost functions and control sources and using hybrid active and passive control techniques can increase the effective frequency range and reduce the number of control sources. Although the feasibility of these two systems has been verified, more research work is needed to develop practical systems. An active-passive hybrid structure for specific application scenarios, which combines these two approaches together as well as the virtual sensor technology and sound field estimation technology, may most likely be practical methods to achieve effective noise reduction near the human ear in a complex sound field in the near future.

Virtual Microphone Control for an Active Noise-Cancelling Casing

An active noise-canceling casing is very attractive for reduction of sound generated bydevices. Such casing can provide good noise reduction for low frequencies, where a passive barrierwould be too thick for practical use. The classical active noise control approach, where the goal is tominimize the sound pressure level around multiple microphones outside the casing can be used. However,it requires placing external microphones, what makes the overall technical solution not acceptedfor many applications. The active vibration control, where the goal is to minimize vibrations of allplates, requires only sensors on the plates. However, in this solution, in turn, noise reduction resultsare worse. This paper presents employment of the idea of the virtual microphone-based approach toimprove results from the system based on vibration sensors only, which are used to estimate acousticpressure at specific locations in the acoustic field. By using a two-stage structure, the system is tunedto reconstruct the same vibrations of the plates, which were present when the acoustic pressure wereminimized directly in the square sense. A laboratory active noise-canceling casing used for experimentsis made of 5 actively controlled aluminum plates mounted on a steel frame. It is passivelyisolated from the floor. On each plate, three electrodynamical actuators are installed. The controlsystem is experimentally verified and obtained results are reported.

Adaptive muffler based on controlled flow valves.

An adaptive muffler with a flexible internal structure is considered. Flexibility is achieved using controlled flow valves. The proposed adaptive muffler is able to adapt to changes in engine operating conditions. It consists of a Helmholtz resonator, expansion chamber, and quarter wavelength resonator. Different combinations of the control valves' states at different operating conditions define the main working principle. To control the valve's position, an active noise control approach was used. With the proposed muffler, the transmission loss can be increased by more than 10 dB in the selected frequency range.

Active Noise Control Experiments using Sound Energy Flu

AbstractThis paper reports on the latest results concerning the active noise control approach using net flow of acoustic energy. The test set-up consists of two loudspeakers simulating the engine noise and two smaller loudspeakers which belong to the active noise system. The system is completed by two acceleration sensors and one microphone per loudspeaker. The microphones are located in the near sound field of the loudspeakers. The control algorithm including the update equation of the feed-forward controller is introduced. Numerical simulations are performed with a comparison to a state of the art method minimising the radiated sound power. The proposed approach is experimentally validated.

A novel multiple-channel active noise control approach with neural secondary-path model for interior acoustic noise attenuation of railway train systems

Interior noise cancellation for railway train systems is an important means of enhancing passenger comfort and quality of service. This study proposes a novel active noise control (ANC) approach that uses an finite impulse response (IIR) filter and neural network techniques to effectively reduce interior noise. The authors construct a multiple-channel IIR filter module that is a linearly augmented framework with a generic IIR model to generate a primary control signal. A three-layer perceptron neural network is employed for establishing a secondary-path model to represent air channels among noise fields. Since the IIR module and neural network are connected in series, the output of an IIR filter is transferred forward to the neural model to generate a final ANC signal. A gradient descent optimisation-based learning algorithm is analytically derived for the optimal selection of the ANC parameter vectors. Moreover, re-estimation of partial parameter vectors in the ANC system is proposed for online learning. Sufficient stability conditions are derived for the proposed ANC system. Lastly, the authors present the results of a numerical study to test their ANC methodology with realistic interior noise measurement obtained from Korean railway trains.

Acoustic characterization and noise reduction strategies for a general aviation aircraft

This work concerns with the vibro‐acoustic characterization of a general aviation aircraft with the objective of designing solutions to reduce the inside noise level during the flight; first of all a new passive insulation, and then verifying the possibility of an active noise control approach. The acoustic characterization was performed into two different step; in‐flight acoustic and vibration measurements during a typical mission of the vehicle were initially measured, to characterize the acoustic levels and spectra elated to the different flight operations. Ground measurement were also performed to characterize the sound insulation properties of the aircraft structure. The next item was the partial re‐design of the sound insulation pack for the vehicle by the use of innovative material; the choice of the best technical solution has been performed by the use of numerical prediction models and through experimental tests on modified samples of the aircraft structure. The in‐flight acoustic and vibration measurement showed also the presence of dominant low frequencies tonal components related to the BPF's and upper harmonics. This situation suggested the possibility to implement an active noise control approach. All these activities will be herein presented and discussion of the main results will be performed within the paper.

Secondary actuation and error sensing for active acoustic structure

A typical approach to active control of sound radiation or transmission from vibrating structures involves active structural acoustic control (ASAC) and active noise control (ANC), which introduce respectively force input and compacted sound source to apply on or be close to the vibrating structure. However, for the ASAC approach, arrangement for secondary force and error sensor is heavily dependent upon the properties of the primary structure and acoustical space; for the ANC approach, a large number of compacted secondary sources are required. Hence, in this paper, based on distributed secondary sound source and near-field error sensor, active acoustic structure is proposed to construct adaptive or smart structure as a versatile module or element for controlling sound radiation or transmission at low frequencies. First, a theoretical model based on a minimization of the total sound radiation from the primary and secondary panel is established, after which, taking into consideration the relationship between the vibration modes pattern and sound radiation characteristics for secondary panels, optimal arrangement for the secondary panels is examined in detail. Finally, a near-field pressure-based error sensing approach is presented, based on two kinds of object function, and active control of sound radiation is performed.

Active Noise Cancellation for a Three-Dimensional Enclosure by Using Multiple-Channel Adaptive Control and H∞ Control

Active noise control (ANC) techniques for a three-dimensional enclosure are compared in terms of two control structures and two control algorithms. The multiple-channel filtered-x least-mean-square (FXLMS) algorithm and the H∞ robust control algorithm are employed for controller synthesis. Both feedforward and feedback control structures are compared. The Youla’s parameterization is employed in the formulation of the multiple-channel feedback FXLMS algorithm. The algorithms are implemented using a floating-point digital signal processor (DSP). Experiments are carried out to validate the ANC approaches for attenuation of the internal field in a rectangular wooden box. Position and number of actuators and sensors are also investigated. A broadband random noise and an engine noise are chosen as the primary noises in the experiments. The experimental results indicate that the feedforward structure yields a broader band of attenuation than the feedback structure. The FXLMS control and H∞, control achieve comparable performance.

Attenuation of broadband noise in a reverberant room using a multichannel active noise control system

Active noise control (ANC) is often discussed in the context of reducing noise in vehicles. The acoustic reverberation of typical vehicle cabins makes the application of ANC to quieting interior noise a stiff challenge. While some ANC approaches take advantage of the modal structure of reverberant enclosures to provide reduction throughout the enclosure, these approaches become infeasible for many frequency bands of practical interest because of increasing modal density with increasing frequency. For high‐bandwidth, diffuse fields, noise reduction is achieved locally by specifying a quiet zone within the enclosure. Experimental results for broadband, local quieting in a diffuse field using a multichannel ANC system are presented. The reverberant enclosure is an ordinary room, measuring approximately 1.4 m×2.4 m×2.4 m and containing a seated operator with six microphones defining the quiet zone near the operator’s ears. The control system uses a single reference signal and two error channels to drive four control speakers. An average of 20‐dB reduction in sound pressure level is found at the quiet‐zone microphones for the frequency range 50 to 1000 Hz. These results demonstrate the viability of real‐time, multichannel ANC for locally attenuating random noise in diffuse acoustic fields.

The passive and active noise control strategy in the aircraft interior noise

The stringent comfort requirements that the airliners are providing to the aircraft manufacturers, also for the small regional commuter are increasing the attention of the aircraft designer to the aircraft interior noise performance. The research on the design methodologies is very much improved in the last years providing new materials and new system like the active noise control usefull to get a more comfortable interior and achieve a large competition on the market. In this paper a general analysis of passive and active noise control approach for the aircraft design is gived providing some indications about the strategy used to select the optimum design approach