Multichannel Active Noise Control System Research Articles

Simulation of active noise control using transform domain adaptive filter algorithms linked with the inhomogeneous Helmholtz equation

An iterative approach is presented that couples the inhomogeneous Helmholtz equation with transform domain active noise control algorithms. The Boundary Element Method is utilized to provide solutions of the inhomogeneous Helmholtz equations for three dimensional pressure wave fields. The source terms in the inhomogeneous Helmholtz equations are adapted iteratively by the algorithm to achieve convergence in the noise cancelling process. The approach can be used to study the impact of active noise control systems based on transform domain algorithms on the wavefield for specific application environments. Additionally, this approach allows for numerical frequency-dependent performance evaluation of multichannel active noise control systems even in acoustically challenging environments. Ill-posed control problems can be identified. The required calculation steps for the integration of the method into existing numerical simulation environments are presented step by step. The study is supplemented by numerical experiments and convergence studies that show good agreement with acoustic theory and analytical results reported in literature.

Noise-reducing Insert for NICU Incubator

Premature infants in the Neonatal Intensive Care Unit (NICU) are at risk for hearing impairment, cognitive development abnormalities, and intraventricular hemorrhaging due to high noise levels. Nearly 400,000 infants are admitted into the NICU in the United States each year, and this number continues to increase. Up to 10% of these preterm infants are diagnosed with hearing impairment in comparison to only .1% for the general pediatric population. Noises from the CPAP machine and other life-sustaining devices along with external human noise in the NICU, sound levels average around 62 dB and can reach up to 120 dB. However, the American Academy of Pediatrics recommends noise levels should be lower than 45 dB. Existing incubators on the market do not address this noise issue. The designed solution is to create an adjustable foam insert that can be fitted to any incubator and used for sound absorption. The foam would line the edges of the incubator to reduce resonance and allow for neonatologists still to have full vision of the inside of the incubator. Additionally, the design contains a bottom piece that perfectly fits a Z-flow device so that the infants can be positioned for maximum health. This design was modeled in Solidworks, and various geometries were tested to improve the design’s effectiveness. Two foams were tested, and a statistical t-test was used to determine which foam had better noise reduction. In comparison to having no foam present in the chamber, the noise level was reduced by over 15 dB. The combination of this foam along with a multi-channel Active Noise Control system would significantly reduce noise levels to below the acceptable limit. This system would consist of one reference microphone that picks up noise, two error microphones to sense residual noise, and two loudspeakers to generate the “anti-noise” signal. Ultimately, this foam insert was found to reduce noise levels so that they are under the recommended limit given by the American Academy of Pediatrics.

Block coordinate descent based algorithm for computational complexity reduction in multichannel active noise control system

Multichannel active noise control (MCANC) is widely regarded as an effective solution to achieve a significantly large noise-cancellation area in a complicated acoustic field. However, the computational complexity of MCANC algorithms, such as the multichannel filter-x least mean square (McFxLMS) algorithm, grows exponentially with an increased channel count. Many modified algorithms have been proposed to alleviate the complexity but at the expense of noise reduction performance. Till now, the trade-off between computational complexity and noise reduction performance has limited the practical implementation of MCANC. The block coordinate descent McFxLMS (BCD McFxLMS) algorithm proposed in this paper substantially reduces the computation cost of an MCANC system, while maintaining the same noise reduction performance as the conventional McFxLMS algorithm. Furthermore, a momentum mechanism is integrated into the BCD McFxLMS in practice to improve the convergence speed. The simulation and experimental results validate the effectiveness of proposed algorithms when dealing with noise in a realistic environment.

Coherence-based performance analysis on noise reduction in multichannel active noise control systems.

Active noise control (ANC) over an extended spatial region using multiple microphones and multiple loudspeakers has become an important problem. The maximum noise reduction (NR) potential over the control area is a critical evaluation variable as it indicates the fundamental limitation of a given ANC system. In this paper, a method to mathematically formulate the NR potential for any given multichannel ANC systems is developed. First, the residual error in the multichannel feedforward ANC system is formulated, and then the multiple-input-multiple-output problem is decomposed into the parallel-channel problem. The total energy of the residual error is further decomposed into three different terms representing (i) the signal coherence between the reference signals and error signals, (ii) the filter, and (iii) the system null space. The experimental results validate the proposed evaluation method and illustrate the effectiveness on the maximum NR performance evaluation for given systems. Using the proposed analyzing method, more insight into the contribution of each component to the NR potential can be achieved.

Performance analysis of a diffusion control method for ANC systems and the network design

Active noise control (ANC) systems have been widely used to reduce noise from indoor or outdoor sources, e.g. traffic, office/factory machines and ventilating systems. Since noise control usually needs to be executed within an extended area, the ANC network that involves a large amount of error microphones and loudspeakers is frequently employed. Distribution of the controller network saves computational burden and yields spatial diversity, which enhances the robustness of the system. This paper studies the diffusion (Diff) control for multi-channel ANC systems using filtered-x (Fx) least mean squares (LMS) algorithms. Since communication between nodes within the network makes it difficult to analyze the performance of the entire system, a comprehensive performance analysis of networked FxLMS algorithm is not available currently in literature to our best knowledge. In this paper, the convergence behavior of the Diff-FxLMS algorithm is investigated. The mean and mean squares difference equations are derived, from which the stability of the networked ANC system is analyzed and the steady-state excess mean square errors (EMSEs) for ANC controllers are obtained. Computer simulations are conducted to compare different control methods and verify the theoretical analysis. A specific 10-node network is studied in terms of the network strategy and the noise reduction performance. Moreover, using the proposed theoretical analysis, a systematic and simple design procedure for Diff-FxLMS based ANC systems is proposed. The usefulness of the theoretical analysis and design procedure is demonstrated by means of a design example.

Incremental Learning Based Adaptive Filter for Nonlinear Distributed Active Noise Control System

Active control of noise for multi-channel applications is affected by the existence of nonlinear primary and secondary paths. There is a degradation in the performance of linear multi-channel active noise control (LMANC) systems based on minimization of sum of squared errors obtained from multiple sensors in presence of nonlinear primary path (NPP) and nonlinear secondary path (NSP) conditions. The NPP and NSP problems are more prominent and challenging for multi-point noise control applications owing to different locations of silent zones and acoustic coupling between secondary sources and error sensors. In order to surmount this problem, an incremental strategy based nonlinear distributed ANC (NDANC) system is developed in this article. The adaptive exponential functional link network (AE-FLN) is employed as an adaptive control unit at the acoustic sensor nodes (ASNs) for the design of NDANC system. The incremental co-operation scheme is utilized to provide uniform noise cancellation in presence of NPP and NSP conditions. Simulation study is conducted extensively to demonstrate the efficiency of the proposed system for different practical NPP and NSP scenarios. The detailed computational load analysis and subjective evaluation of reduction in perceptual noise levels are performed for different real noise conditions.

Distributed Wave-Domain Active Noise Control Based on the Diffusion Adaptation

Conducting the spatial active noise control (ANC) in wave-domain has been shown advantageous over conventional point-based methods. In the existing schemes, signals at all error microphones are collected and processed in a centralized manner to update the secondary source driving signals. The high computational complexity of this centralized strategy represents one of the major challenges for applying multi-channel ANC systems in large-scale applications. In order to address this issue, this work presents a distributed wave-domain ANC scheme by resorting to distributed optimization techniques. The global ANC problem is formulated as a sum of local costs, and the diffusion adaptation strategies are subsequently utilized to provide a distributed solution that only requires local information exchanges. Simulation results show that the proposed algorithms achieve sufficiently good performance compared to its centralized counterpart.

Multichannel feedforward active noise control system combined with noise source separation by microphone arrays

In this paper, we propose a multichannel active noise control (ANC) system combined with noise source separation by microphone arrays. A multichannel feedforward ANC system uses multiple reference microphones located close to noise sources and can reduce various acoustic noises such as broadband noise. In general, it is difficult to place the reference microphones close to the noise sources owing to physical constraints. In this case, the reference signals, which are the outputs of reference microphones, are highly correlated with each other, the noise reduction performance of the multichannel ANC system is degraded. In other words, the reference microphones detect the highly correlated noises, reducing the noise reduction ability of the ANC system. To solve this problem, a multichannel feedforward ANC system with a noise source separation technique is proposed. This system uses microphone arrays located at the reference microphone positions. The mixed signal is separated from the target noise and other components by noise source separation and the separated signals are used to update the noise control filters. Simulation results demonstrate that the proposed ANC system can separate the target noise and the disturbance noise and effectively reduce the target noise.

Multichannel Feedforward Active Noise Control System with Optimal Reference Microphone SelectorBased on Time Difference of Arrival

In this paper, we propose a multichannel active noise control (ANC) system with an optimal reference microphone selector based on the time difference of arrival (TDOA). A multichannel feedforward ANC system using upstream reference signals can reduce various noises such as broadband noise by arranging reference microphones close to noise sources. However, the noise reduction performance of an ANC system degrades when the noise environment changes, such as the arrival direction. This is because some reference microphones do not satisfy the causality constraint that the unwanted noise propagates to the control point faster than the anti-noise used to cancel the unwanted noise. To solve this problem, we propose a multichannel ANC system with an optimal reference microphone selector. This selector chooses the reference microphones that satisfy the causality constraint based on the TDOA. Some experimental results demonstrate that the proposed system can choose the optimal reference microphones and effectively reduce unwanted acoustic noise.

Mixed-error approach for multi-channel active noise control of open windows

This paper presents an attempt to reduce the computational complexity, while achieving acceptable level of noise reduction in a multi-channel active noise control (MCANC) system for mitigating noise passing through open windows. The reference signals are sensed at the back of the secondary loudspeakers. The existing approach to reduce the computational complexity has already used only one reference signal in each feedforward channel. As the number of error microphones is also important to ensure global noise reduction of the MCANC system for open windows, the main idea of this paper is to preprocess the error signals so that the number of inputs to the controller is further reduced. When the preprocessing is a simple summation, it is called the mixed-error approach. The number of inputs related to the error signals is eventually reduced to 1 after the mixed-error approach is applied. Simulation results demonstrate that the optimum control filters derived under general conditions can lead to similar global noise reduction to those derived based on the mixed-error approach. Experimental results confirm that in a 4-channel MCANC system applied to a 0.2m by 0.2m open window, the performance of global noise reduction is not compromised by the mixed-error approach.

Gain Scheduling of Auxiliary Noise and Variable Step-Size for Online Acoustic Feedback Cancellation in Narrow-Band Active Noise Control Systems

In this paper, we investigate active noise control systems, which use auxiliary white Gaussian noise for online feedback path modeling and neutralization. Such systems comprise three adaptive filters: 1) active noise control filter, 2) linear prediction filter, and 3) feedback path modeling and neutralization filter. The injection of white Gaussian noise with large/small variance improves/degrades the convergence of feedback path modeling and neutralization filter but on the other hand degrades/improves the noise-reduction performance of active noise control systems. The focus of this paper is to have an adequate compromise between the convergence of feedback path modeling and neutralization filter and the noise-reduction-performance of active noise control systems. In this paper: 1) A tuning free gain scheduling of white Gaussian noise is used to improve the noise-reduction performance of active noise control systems, and 2) a variable step-size is used to compensate for the decrease in the convergence of feedback path modeling and neutralization filter due to gain scheduling. The proposed idea is explained for a single channel as well as for multichannel active noise control systems. Computer simulations have been carried out to demonstrate the performance of the proposed method for a single channel as well as for multichannel active noise control systems.

Multichannel Filtered-X Error Coded Affine Projection-Like Algorithm with Evolving Order

Affine projection (AP) algorithms are commonly used to implement active noise control (ANC) systems because they provide fast convergence. However, their high computational complexity can restrict their use in certain practical applications. The Error Coded Affine Projection-Like (ECAP-L) algorithm has been proposed to reduce the computational burden while maintaining the speed of AP, but no version of this algorithm has been derived for active noise control, for which the adaptive structures are very different from those of other configurations. In this paper, we introduce a version of the ECAP-L for single-channel and multichannel ANC systems. The proposed algorithm is implemented using the conventional filtered-x scheme, which incurs a lower computational cost than the modified filtered-x structure, especially for multichannel systems. Furthermore, we present an evolutionary method that dynamically decreases the projection order in order to reduce the dimensions of the matrix used in the algorithm’s computations. Experimental results demonstrate that the proposed algorithm yields a convergence speed and a final residual error similar to those of AP algorithms. Moreover, it achieves meaningful computational savings, leading to simpler hardware implementation of real-time ANC applications.

Open loop active control of noise through open windows

Noise propagating through open windows is a big concern of liveability in metropolises, where residential buildings and main roads are built densely. Active noise control (ANC) is a convincing technique to improve the current situation. Classic ANC systems adopt the close loop control. The quietness after control is continuously monitored by error microphones. However, ANC systems that attenuate noise propagating through open windows should ideally avoid the use of error microphones for the ease of implementation and maintenance. Furthermore, as open windows are commonly large as compared to the wavelength of noise, multichannel ANC systems are necessary to be carried out. They result in huge computational burdens that cannot be handled by low-cost hardware platforms. This paper investigates acoustic characteristics of open windows and formulates the control strategies as a spatial sampling and reconstruction problem. This theoretical development prompts the fact that controlling noise propagating through open windows essentially leads to global noise reduction. Both centralized and decentralized open loop control are feasible to deal with broadband noise. The upper bound of the effective band is limited by the spacing between channels, while the lower bound is dependent on the frequency response of secondary sources.

능동소음제어를 위한 Adjoint-LMS 알고리즘의 강인성 개선

Noise problem that occurs in living environment is a big trouble in the economic, social and environmental aspects. In this paper, the filtered-X LMS algorithms, the adjoint LMS algorithms, and the robust adjoint LMS algorithms will be introduced for applications in active noise control(ANC). The filtered-X LMS algorithms is currently the most popular method for adapting a filter when the filter exits a transfer function in the error path. The adjoint LMS algorithms, that prefilter the error signals instead of divided reference signals in frequency band, is also used for adaptive filter algorithms to reduce the computational burden of multi-channel ANC systems such as the 3D space. To improve performance of the adjoint LMS ANC system, an off-line measured transfer function is connected parallel to the LMS filter. This parallel-fixed filter acts as a noise controller only when the LMS filter is abnormal condition. The superior performance of the proposed system was compared through simulation with the adjoint LMS ANC system when the adaptive filter is in normal and abnormal condition.

Multichannel active noise control system using variable step size algorithms

This paper presents the feed-forward multichannel active noise control which was simulated on LabVIEW and experimented on two channel feed-forward active noise control in real-time. The simulation of feed-forward multichannel active noise control system was built on LabVIEW by using FxLMS algorithm and other algorithms such as Variable Step-size (VSS) and saturation compensation. Moreover, LMS and VSS LMS algorithm were used to train the secondary path in order to improve the convergence speed and stabilization of ANC system in the real-time. The experimental results show that the ANC system in real-time using variable step-size and without using variable step-size algorithm work effectively with noise sources which were synthesized from one to three frequencies. The noise level in these cases decreased around 12 dB - 35 dB and the noise reduction radius measured is about 3 cm around the microphone.

The frequency partitioned block modified filtered-x NLMS with orthogonal correction factors for multichannel Active Noise Control

The Normalized Least Mean Square (NLMS) algorithm with a filtered-x structure (FxNLMS) is a widely used adaptive algorithm for Active Noise Control (ANC) due to its simplicity and ease of implementation. One of the major drawbacks is its slow convergence. A modified filtered-x structure (MFxNLMS) can be used to moderately improve the speed of convergence, but it does not offer a huge improvement. A greater increase in the speed of convergence can be obtained by using the MFxNLMS algorithm with orthogonal correction factors (M-OCF), but the usage of orthogonal correction factors also increases the computational complexity and limits the usage of the M-OCF in massive real-time applications. However, Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform to ameliorate this computational drawback. In this paper, we propose to derive the M-OCF algorithm to a partitioned block-based version in the frequency domain (FPM-OCF) for multichannel ANC systems in order to better exploit the parallel capabilities of the GPUs. The results show improvements in the convergence rate of the FPM-OCF algorithm in comparison to other NLMS-type algorithms and the usefulness of GPU devices for developing versatile, scalable, and low-cost multichannel ANC systems.

Real-time implementation of multi-channel active noise control systems for infant incubators

High noise in infant incubators results in numerous adverse health effects, which have lifelong consequences for neonatal intensive care unit (NICU) graduates. This paper presents multi-channel feed forward active noise control (ANC) systems for infant incubators. Then, the proposed algorithm is implemented by using Texas Instrument TMS320 C6713. The real-time experiment results show that the proposed algorithm is effective in canceling the noises of infant incubators in real NICU environment

GPU Implementation of Multichannel Adaptive Algorithms for Local Active Noise Control

Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.

A Computationally Efficient Multichannel Active Road Noise Control System

A multichannel active noise control (ANC) system has been developed for a vehicle application, which employs loudspeakers to reduce the low-frequency road noise. Six accelerometers were attached to the vehicle structure to provide the reference signal for the feedforward control strategy, and two loudspeakers and two microphones were applied to attenuate acoustic noise near the headrest of the driver's seat. To avoid large computational burden caused by the conventional time-domain filtered-x least mean square (FXLMS) algorithm, a time-frequency domain FXLMS (TF-FXLMS) algorithm is proposed. The proposed algorithm calculates the gradient estimate and filtered reference signal in the frequency domain to reduce the computational requirement, while also updates the control signals in the time domain to avoid delay. A comprehensive computational complexity analysis is conducted to demonstrate that the proposed algorithm requires significantly lower computational cost as compared to the conventional FXLMS algorithm.

Nonlinear adaptive filter-based simplified bilinear model for multichannel active control of nonlinear noise processes

To circumvent nonlinear distortions which occur in multichannel nonlinear active noise control (ANC) systems used for practical applications, a simplified bilinear leaky filtered-x least mean square (SBLFXLMS) algorithm for nonlinear adaptive filter is presented in this paper. The performance of the simplified bilinear adaptive filter is evaluated in terms of computational complexity and convergence characteristics. Computer simulations have been carried out to verify that the nonlinear adaptive filter with the SBLFXLMS algorithm is more effective in eliminating nonlinear distortions in multichannel nonlinear ANC systems than the linear FXLMS, second-order Volterra FXLMS (VFXLMS) and filtered-s LMS (FSLMS) algorithms. Furthermore, the analysis of the computational requirements and simulations results both show that the SBLFXLMS algorithm requires less computations without suffering from any performance degradation of mutichannel nonlinear ANC systems.