Active Noise Control Performance Research Articles

Data-Driven Uncertainty Modeling for Robust Feedback Active Noise Control in Headphones

Active noise control (ANC) has become popular for reducing noise and thus enhancing user comfort in headphones. Although feedback control offers an effective way to implement ANC, it is restricted by uncertainty of the controlled system (e.g., that arises from differing wearing situations). Widely used unstructured models that capture these variations tend to overestimate the uncertainty and thus restrict ANC performance. As a remedy, this work explores uncertainty models whose shapes are derived from experimentally determined measurement data to improve ANC performance for over-ear and in-ear headphones. The controller optimization based on these models is described, and an ANC prototype is implemented to compare the performances associated with conventional and proposed modeling approaches. Extensive measurements with human wearers confirm the robustness and indicate a performance improvement over conventional methods. The results allow to increase the active attenuation of ANC headphones by several decibels at no loss of stability.

Model-free active noise control using second-order simultaneous perturbation stochastic approximation algorithm for periodic noise

Model-free active noise control (ANC) using simultaneous perturbation stochastic approximation (SPSA) algorithm has recently attracted interest of researchers since it avoids the problem of secondary path modeling (SPM). However, the existing SPSA-based ANC methods are all based on the first-order SPSA algorithm and the convergence rate of these methods are significantly lower than that of the Filtered-x least mean square (FxLMS) algorithm. In order to enhance the convergence performance, this paper introduces the second-order SPSA algorithm into the ANC system. During the implementation of the second-order SPSA algorithm, a novel method of matrix positive definite transformation based on eigenvalue decomposition (EVD) is proposed. Convergence performance and tracking performance of ANC using the second-order SPSA algorithm are compared with ANC using the FxLMS algorithm and the standard SPSA through simulation and experimental verification. Simulation and experimental verification results show that ANC using the second-order SPSA algorithm can not only achieve the convergence rate comparable to that of ANC using the FxLMS algorithm, but also track the secondary path change.

A time-domain study on the effects of noise source incidence on plenum window with active noise control

Active noise control (ANC) applied to the built environment on windows presents itself as a potential solution to reduce noise exposure for residents living near major noise sources since windows are typically the primary path for noise intrusion on a building's facade. The plenum window design seeks to address the dual dichotomy of ventilation and acoustic dilemma that plagues hot-humid and highly noise-polluted cities. In the present study, a time-domain approach is developed to investigate the effect of varying impinging angles of noise sources on the acoustic performance of a plenum window which can represent different storeys of the receiving end of noise exposure. Furthermore, the ANC performance of a control system affected by the varying noise sources is explored in this study. While most ANC studies have limited their scope of study to frequency domain investigations, the present study takes a time-domain approach to account for the causality of the ANC system concerning the primary noise source and secondary control source, which is thus more accurate to the behaviour of a real-time ANC controller. A total of 121 simulation studies were performed, and the findings show that the plenum window's PNC (passive noise control) is affected by the incident angle of a noise source based on its tonal frequency, which is pronounced near the aperture's cut-off frequency, faperture. However, the ANC performance due to varying incident angles of primary noise remains unaffected even at high frequencies with a standard deviation, σANC < 1 dB except for the 630 Hz tonal frequency, which coincides with faperture.

Optimization of single-channel active noise control performance in a plenum window using the surface impedance approach.

The use of active noise control (ANC) implementation in plenum window design is investigated in this study. Various simulated configuration of a single-channel ANC is performed using the surface impedance approach (SIA) in order to optimize ANC performance. Based on a systematic search procedure, the optimal control source placement is found for a control source localized at the central bottom and central depth of the plenum window, near the window's inlet from which primary noise is impinging. The optimized ANC configuration provides an average attenuation benefit of 9.2 dB between 200 and 630 Hz. Error sensor location in the plenum window cavity is not crucial for the ANC system and does not need to be rigid. A dual-channel ANC system with control sources at both sides of the plenum window can extend the frequency of control to ∼800 Hz with an average attenuation of 7.6 dB. Additionally, an experimental case study using a real-time ANC system is conducted with a built-to-scale plenum window in an apartment informed by findings from the SIA simulation, demonstrating the usefulness of the SIA in ANC optimization process.

Active noise control performance in the high-frequency range

Noise control for vehicle mainly uses a reduction method through sound absorbing and insulating materials but has disadvantages of increasing cost and weight. It is known that the noise reduction performance of Active Noise Control (ANC) technology that can be considered as an alternative is limited to the low frequency range. However, there could be a case when the use of active noise cancellation is required at higher frequency range. Therefore, the purpose of this paper is to find out how much high-frequency noise can be controlled by applying active noise cancellation and the performance of ANC according to the equipment used. For this end, an ANC experimental environment was installed at the passenger's seat interior vehicle. By using Filtered-x Least Mean Square (FxLMS) algorithm based on adaptive control, ANC was conducted to reduce the high frequency noise in the 2kHz~4kHz and its noise reduction performance was compared according to the following variables: Filter length, step size, sampling frequency, etc. As a result of selecting the audio DSP board as the control system and applying appropriate filter length, step size, and sampling frequency, noise reduction of up to 40 dB(A) was obtained at the position of both ears of the occupant.

Enhancing active noise control of road noise using deep neural network to update secondary path estimate in real time

The performance of active noise control (ANC) is significantly influenced by the accuracy of the secondary path estimate. In the case of a vehicle, dynamic environments, which constantly change the secondary path, can degrade the ANC performance. To address this issue, we proposed a deep learning-based method to continuously update the secondary path estimate in real time. The sensitivity of secondary paths to various conditions was analyzed, with vertical, horizontal, roll, and yaw conditions identified as primary factors; subsequently, a dataset corresponding to these conditions was created. Estimation models were formulated using Delaunay triangulation-based interpolation and three deep neural network (DNN)-based methods which incorporated diverse representations of the secondary path domain. All the methods precisely estimated the secondary path under different conditions. Notably, DNN-based methods required significantly less data storage than the interpolation method, with the principal component analysis (PCA)-based approach performing the best, achieving a 98.5 % reduction. Using this method, updating the secondary path estimate in real time led to reductions of 10.2 and 17.2 dB in road noise and 500–1,000 Hz random noise, respectively. The improvement was notable for both types of noises, indicating that the suggested method can expand the frequency range of ANC.

Low-Latency Active Noise Control Using Attentive Recurrent Network.

Processing latency is a critical issue for active noise control (ANC) due to the causality constraint of ANC systems. This paper addresses low-latency ANC in the context of deep learning (i.e. deep ANC). A time-domain method using an attentive recurrent network (ARN) is employed to perform deep ANC with smaller frame sizes, thus reducing algorithmic latency of deep ANC. In addition, we introduce a delay-compensated training to perform ANC using predicted noise for several milliseconds. Moreover, a revised overlap-add method is utilized during signal resynthesis to avoid the latency introduced due to overlaps between neighboring time frames. Experimental results show the effectiveness of the proposed strategies for achieving low-latency deep ANC. Combining the proposed strategies is capable of yielding zero, even negative, algorithmic latency without affecting ANC performance much, thus alleviating the causality constraint in ANC design.

Wind noise suppression in filtered-x least mean squares-based active noise control systems.

Wind noise is notorious for its detrimental impacts on audio devices. This letter evaluates the influence of wind noise on the active noise control performance of headphones in a wind tunnel, and the noise reduction is found to decrease with wind speeds. To improve the performance of noise control systems in windy environments, the filtered-x least mean squares algorithm is modified based on the total least squares technique, taking the characteristics of wind noise into account. Computer simulations with real-recorded data demonstrate that the proposed algorithm could improve the noise reduction by approximately 3 dB in windy conditions.

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.

Active Noise Reduction with Filtered Least-Mean-Square Algorithm Improved by Long Short-Term Memory Models for Radiation Noise of Diesel Engine

This study presents an active noise control (ANC) algorithm using long short-term memory (LSTM) layers as a type of recurrent neural network. The filtered least-mean-square (FxLMS) algorithm is a widely used ANC algorithm, where the noise in a target area is reduced through a control signal generated from an adaptive filter. Artificial intelligence can enhance the reduction performance of ANC for specific applications. An LSTM is an artificial neural network for recognizing patterns in arbitrarily long sequence data. In this study, an ANC controller consisting of LSTM layers based on deep neural networks was designed for predicting a reference noise signal, which was used to generate the control signal to minimize the noise residue. The structure of the LSTM neural networks and procedure for training the LSTM controller for the ANC were determined. Simulations were conducted to compare the convergence time and performances of the ANC with the LSTM controller and those with a conventional FxLMS algorithm. The noise source adopted sounds from a single-cylinder diesel engine, while reference noises selected were single harmonics, superposed harmonics, and impulsive signals generated from the diesel engine. The characteristics of each algorithm were examined through a Fourier transform analysis of the ANC results. The simulation results demonstrated that the proposed ANC method with LSTM layers showed outstanding noise reduction capabilities in narrowband, broadband, and impulsive noise environments, without high computational cost and complexity relative to the conventional FxLMS algorithm.

Compact hybrid noise control system: ANC system equipped with circular noise barrier using theoretically calculated control filter

This paper proposes and investigates the feasibility of a compact noise control system for a wide frequency band, which can be easily installed and calibrated. Noise reduction over the workspace can be achieved by noise barriers, but becomes ineffective in low-frequency. Active noise control (ANC) is effective noise control method in low-frequency, so hybrid noise control systems combining a barrier and ANC were proposed. However, previous researches aimed at global noise control behind a large barrier for a stationary environment, which is very costly and difficult to reinstall. Despite its advantages in terms of space, cost, and mobility, a compact hybrid noise control system which is ANC system equipped with a compact size barrier for a specific space has been rarely studied due to the complex scattering effects and microphone arrangements in the control space. The present study aimed to investigate the feasibility of a compact hybrid noise control system using a theoretically calculated control filter, without microphones arrangement. Thus, can be various shapes of barrier and speaker arrangements, in this study, an axisymmetric structure is dealt with, since the theoretical equations had already been established and the computational amounts are reduced by simplifying it to two-dimensional problems. Theoretical noise reduction performance of a barrier, ANC, and the proposed noise control system are compared. Performance of ANC decreases with increasing frequency, and barrier is only effective in high-frequency bands. In contrast, the proposed noise control system achieved satisfactory noise reduction throughout the entire frequency range of interest. Following that, applying the theoretically calculated control filter was validated through experiments. The results showed that the proposed noise control system achieved a noise reduction of about 8 dB for a band-limited white noise in which the frequency range is 200–2000 Hz.

Delayless polyphase implementation of filters in active noise control systems

In real-time implementation of active noise control filters, high sampling rate is sometimes preferred to reduce the delay in analog-to-digital converters or to reduce the risk of aliasing effect. A multi-rate structure is usually implemented with a polyphase structure to maintain a low sampling rate for the control filters to avoid the increase of real-time computational load. However, the use of decimation or interpolation filter will introduce additional delay, which can negatively affect the noise control performance. In the current work, a delayless implementation of polyphase structure is proposed where the original active noise control filter is first constrained to have sufficient attenuation at high frequencies in its design phase and is then decomposed into two multiplicative sub-filters, each of which involves a sufficiently high frequency attenuation. These two decomposed sub-filters can perform both noise control and decimation/interpolation without introducing additional delay in the polyphase structure. The proposed delayless approach can thus reduce the delay of a traditional multi-rate active noise control system and, in principle, can provide better active noise control performance. Moreover, if a single rate system is implemented at a high sampling rate, the proposed polyphase structure in filter implementation can effectively reduce the real-time computational load.

Constrained optimal filter design for multi-channel active noise control via convex optimization.

In many practical multi-channel active noise control (ANC) applications, various constraints need to be satisfied, such as stability, enhancement, etc. One way to enforce these constraints is to add a regularization term to the Wiener filter formulation, which, by tuning only a single parameter, can over satisfy many constraints and degrade the ANC performance. Another approach for non-adaptive ANC filter design that can produce better ANC performance is to directly solve the constrained optimization problem formulated based on the H2/H∞ control framework. However, such a formulation does not result in a convex optimization problem and its practicality can be limited by the significant computation time required in the solving process. In the presented work, the H2/H∞ formulation is convexified and a global minimum is guaranteed. It is then further reformulated into a cone programming problem which can be solved using specialized algorithms. Results show that the proposed method can produce better noise control performance than the regularization method. Compared with the traditional H2/H∞ formulation, the proposed method is more reliable and the computation time can be reduced by several orders, which, practically, provides a potential to extend its application to adaptive control.

Influence of the inclination angle on the sound field in an irregular enclosure containing two elastic walls

The sound field model of an irregular enclosure with two elastic walls and an inclined wall is established by the modal theory. The modal parameters of the irregular enclosure are obtained by the envelope rectangular technique. The influence of the inclination wall angle on the sound field in the irregular enclosure is discussed. When the inclination angle is increased from 0° to 45°, the resonance frequencies of the acoustic enclosure are basically reduced in the frequency range of 0∼250 Hz. When the inclination angle is increased from 0° to 45° every 15° interval, the amplitude of a certain acoustic mode itself decreases, while the amplitudes of the acoustic modes coupled to it basically increase. The acoustic potential energy peaks in the enclosure basically shift to the low frequency with the increase of the inclination angle. Furthermore, the change trend of the acoustic potential energy is regular in the low-order modes, but relatively chaotic in the high-order modes. In addition, in order to verify that the accurate description of the primary sound field is the basis of effective noise control, the two-elastic plate model is deliberately treated as one-elastic plate model, and its influence on the performance of active noise control is discussed. Also, if the multi-elastic plate model is regarded as a simplified automobile cab, the research results can be used for the preliminary acoustic design of the cab; that is, the research results can be used for the acoustic design of similar models.

Optimal parametric design of delayless subband active noise control system based on genetic algorithm optimization

Active noise control systems are generally application-specific, and an appropriate algorithm with an optimal configuration is desirable in the first stage of active noise control system design and deployment. This study presents a design of the subband active noise control system with optimal parameters for a practical broadband active noise control. Although the delayless subband active noise control has gained wide attention for broadband noise cancellation, an optimal design remains a challenge because of the complex interplay between multiple factors such as spectral leakage, delay and weight stacking distortion subject to a number of configurable parameters, and weight stacking method. The configurable parameters can hardly be optimized independently because the active noise control performance depends on the combined configuration. A simple near black box active noise control algorithm optimization model is thus established by incorporating the genetic algorithm optimization into the parametric design of the delayless subband algorithm. The automated process does not require an understanding of the performance characteristics for different parameters. The significance of applying the automated genetic algorithm optimization to the complex multiparameter nonlinear active noise control design is revealed by numerical simulations, particularly for the multichannel low-frequency broadband active noise control system configured with the delayless subband algorithms. This provides a way for the optimal parametric design of subband active noise control before being used in a practical complex scenario.

The performance of active noise control systems on ground with two parallel reflecting surfaces

This paper investigates the performance of active noise control (ANC) systems with two reflecting surfaces that are placed vertically on ground in parallel. It employs the modal expansion method and the boundary element method to calculate the noise reduction of the systems with infinitely large and finite size reflecting surfaces, respectively. Both experimental and simulation results show that the noise reduction of the system can be significantly increased after optimizing the surface separation distance and their locations with the sound sources. It is found that the sound radiation of the primary source can be completely reduced in principle if the surface interval is less than half the wavelength and the source line is perpendicular to the surfaces for infinitely large reflecting surfaces. Even with finite size ones, the noise reduction performance improvement is still significant compared with those without any reflecting surfaces. For example, for an ANC system with a source distance of 0.074 m, experiments achieve an improvement of 8.6 dB at 800 Hz where two 0.2 m × 0.2 m parallel reflecting surfaces are placed with a distance of 0.15 m around the system on ground. The mechanisms for the performance improvement are discussed.

Noise reduction performance of active noise control with barrier using theoretical control filter

The external noise is a problem closely related to modern society. There are two ways of noise control methods. One is passive noise control that uses absorbing or insulating materials. The other is active noise control that generates the sound of the opposite phase with the noise by the secondary speakers. Since the effective frequency band of the noise reduction in each method is different, using both methods has been researched to expand the controllable frequency band for dealing with various noise. In order to apply active noise control, the information over the control region is required to obtain a control filter of control speaker. However, the general method that arranges the microphones over the control region can be the obstacles to the user, therefore the other method is needed to obtain the control filter. One way to obtain the control filter without the arrangement of the microphones is using the theoretical model and the control filter can be obtained relatively easily based on the theoretical model. Therefore, the theoretical model is generally used to estimate the maximum performance by simulation. However, there is less research for studying the applicability of the ANC system with a finite noise barrier based on the theoretical model and the verification of the control filter is required by investigating noise reduction performance in the experiment. In this paper, the control filter is obtained based on the theoretical model and noise reduction performance is investigated in the experiment for verification of the control filter.

A Realistic Multiple Circular Array System for Active Noise Control Over 3D Space

Spatial active noise control (ANC) systems focus on minimizing unwanted acoustic noise over a continuous spatial region. Conventionally, spatial ANC systems are proposed using point based multi-channel systems and recently novel methods have been developed using spherical harmonic analysis of spatial sound fields. A major limitation for implementing the latter approach is the requirement of regularly distributed microphones and loudspeakers over spherical surfaces. In this paper, we relax the above constraint by constructing a system utilizing multiple circular microphone and loudspeaker arrays and designing a corresponding ANC algorithm. By simulation, we show that the proposed method can achieve comparable ANC performance to conventional spherical array methods. By experiment, we demonstrate the feasibility of implementing the multiple circular array structure and verify its effectiveness given practical constraints.

An Efficient Narrowband Active Noise Control System for Accommodating Frequency Mismatch

The conventional narrowband active noise control (ANC) is a popular method for reducing narrowband noise generated from rotating machines like engines, fans, blowers, and power transformers. The narrowband active noise control works efficiently only when the internal reference frequency of the controller and the frequency of the primary noise remains the same. Any change in the frequency of the primary noise from that of the reference is termed as frequency mismatch (FM), which degrades the narrowband ANC performance. In this paper, a filtered-x weighted-frequency Fourier linear combiner least mean square (FX-WFLC-LMS) algorithm is developed for narrowband ANC system. This algorithm is capable of adapting to both frequency and amplitude variations in the primary noise. To reduce the computational burden of the proposed FX-WFLC-LMS algorithm, a computationally efficient filtered-error weighted-frequency Fourier linear combiner least mean square (FE-WFLC-LMS) algorithm is also proposed. The comparative performance of these proposed algorithms is evaluated through extensive simulation and real-time experiments. It was found that both these proposed algorithms are capable of correcting any amount of frequency mismatch and are suitable for narrowband ANC systems.

Increasing the performance of active noise control systems on ground with two vertical reflecting surfaces with an included angle.

This paper investigates the feasibility of increasing the noise reduction performance of active noise control (ANC) systems on ground by introducing two vertical reflecting surfaces with an included angle. By using the image source method, the theory of sound wave propagation in a wedge-shaped reflector and the integral equation method, the noise reduction of the ANC systems with two infinitely large or finite size reflecting surfaces with different included angles are studied. It is demonstrated that the noise reduction of the system can be increased significantly with two reflecting surfaces after optimizing their included angle and size. The simple empirical formulas for the optimal included angle of the surfaces and the noise reduction are presented. It is found that the noise reduction at 500 Hz increases by 13.6 dB when two vertical reflecting surfaces are arranged with an optimal angle of 125° and the source distance is 0.1 m. By optimizing the size of the reflecting surfaces to about 0.35 of the wavelength, the noise reduction can be further increased by approximately 2.8 dB. The mechanisms for the performance improvement are disclosed, and the experiments are conducted to validate the results.