Multichannel Active Noise Control System Research Articles

Enhanced selective delayless subband algorithm independent of primary disturbance configuration for multi-channel active noise control system in vehicles

The selective delayless subband structure stands out as a promising algorithmic choice for the multi-channel active control of vehicle interior noise, particularly in the context of road noise. This type of algorithm reduces the eigenvalue spread of the autocorrelation matrix of the signal by decomposing the signal into subbands, and the desired subbands are activated selectively, thus achieving a significant performance improvement while consuming less computational resources compared with the traditional fullband algorithms. Nevertheless, the effectiveness of the subband algorithm appears to be closely tied to the configuration of the primary disturbance signal. In cases where the primary disturbance signal encompasses both broadband and tonal components, the performance advantage of the subband algorithm becomes constrained. In this paper, we conduct a thorough comparative analysis of two extensively employed subband algorithms, namely the Morgan and the Milani methods, through simulations employing data obtained from a multi-channel active noise control (ANC) headrest system. The results indicate that the Morgan method outperforms the Milani method when configured optimally. Subsequently, we propose an enhanced version of the subband algorithm based on the Morgan method. The enhanced algorithm incorporates an additional sinusoidal noise canceller (SNC) subsystem and a narrowband active noise control (NANC) subsystem based on local secondary path modeling to address tonal components, while the selective subband structure is employed for controlling broadband components. In addition, the computational complexity of the algorithm is analyzed. The effectiveness of the proposed algorithm is validated through numerous simulations and the ANC tests conducted on an actual vehicle utilizing the multi-channel ANC headrest system. The performance of the proposed algorithm surpasses that of traditional selective subband and fullband algorithms, and balanced binaural noise reduction is achieved.

The Effectiveness of Least Mean Squared-Based Adaptive Algorithms for Active Noise Control System in a Small Confined Space

Active noise control (ANC) is a technique applied to eliminate an unwanted sound by superposing a signal of equal amplitude and opposite phase, sometimes defined as an anti-noise signal, computed through an adaptive algorithm. The study described herein aims to evaluate and compare the performance of some of the most popular algorithms based on the least mean squares (LMS) approach applied to a multichannel active noise control system in a small, enclosed space. The comparison is conducted through an experimental evaluation of the ANC algorithms’ performance, carried out on a tractor cabin in a hemi-anechoic chamber, generating the unwanted sound field using a dodecahedron sound source placed outside the enclosure, emitting narrowband and broadband signals. The experimental analysis and the comparison with the results obtained in a free field condition have made it possible to show certain practical limitations when implementing the algorithms. The results show that the feed-forward systems allow for greater stability, avoiding the acoustic feedback from the control loudspeakers to the reference microphone when this is outside the cabin, while the feedback system is the slowest configuration to converge, requiring an internal modeling of the reference signal. With random signals, the feed-forward systems concentrate their performance in the range above 500 Hz, while the feedback system becomes ineffective.

A hat-shape error microphone array for user-friendly spatial active noise control

Spatial active noise control (ANC) has been developed in the past decades to reduce unwanted acoustic noise over a desired region by generating an anti-noise field with secondary loudspeakers. Unlike conventional multi-channel ANC systems, the integration of spherical harmonic-based sound field analysis allows the ANC system to reduce noise over a continuous region surrounding the user's head rather than discrete points. However, the use of a spherical error microphone array on the boundary of the region for spherical harmonic analysis poses a limitation as it obstructs user access to the controlled area. In this work, we introduce a hat-shaped error microphone array design which can be placed above the listener's head. The proposed error microphone array can achieve spherical harmonic-based residual noise field analysis and is more user-friendly, allowing unrestricted user head movement. We construct the hat-shaped error microphone array and present the results of ANC experiments conducted using the array.

A low-complexity multi-channel active noise control system using local secondary path estimation and clustered control strategy for vehicle interior engine noise

Conventional multi-channel active noise control (ANC) system based on the adaptive notch filtered-x least mean square algorithm is typically used for active control of vehicle interior engine noise with prominent harmonic characteristics. However, due to the large estimated secondary path length and the centralized control strategy, the system has high computational complexity and fragile stability. To alleviate this problem, an efficient multi-channel ANC system using the local secondary path (LSP) estimation and clustered control strategy is proposed in this paper. Based on the modified LSP estimation method, the system may utilize a set of low-order but accurate LSP models to simplify the convolution operations of reference filtering. Besides, the proposed clustered control strategy combines the advantages of centralized and decentralized control strategies, and thus the system not only enjoys good noise attenuation performance and stability, but also its computational burden is further greatly reduced. Numerical simulations are performed to evaluate the noise attenuation performance and frequency mismatch effect of the proposed multi-channel ANC system. In addition, a series of real vehicle ANC experiments are conducted. The results show that, under stationary work conditions, the interior overall, 2nd order, 4th order, 6th order engine noises may be attenuated by 12.91 dB(A), 32.98 dB(A), 15.34 dB(A) and 8.74 dB(A), respectively, and under nonstationary work conditions, the maximum overall noise reductions at the four error microphones may reach 6.40 dB(A), 6.14 dB(A), 12.48 dB(A) and 11.07 dB(A), respectively. This fully confirms the effectiveness of the proposed multi-channel ANC system in real-world applications.

A new variable spatial regularized FxLMS algorithm for diffusion active noise control

Distributed multi-channel active noise control (ANC) systems attract a lot of attention due to the reduced computational complexity than centralized control methods and improved stability than decentralized control methods. However, the combination of controllers within a neighborhood in a diffusion manner introduces an estimation bias and may degrade the control accuracy. This is because the secondary sources and error microphones of an ANC system are usually physically placed at different locations and the optimal solution to each controller is different. In this paper, a new diffusion filtered-x least mean squares algorithm (Diff-FxLMS) has been developed that balances combination strength and estimation bias via a variable spatial regularization. The mean squares error criterion subject to a bias constraint is used such that the spatial regularization parameter could be adapted according to the penalized Lagrangian. A detailed performance analysis is carried out, based on which user parameters can be selected automatically. Performance of the proposed variable spatial regularized Diff-FxLMS (VSR-Diff-FxLMS) algorithm and theoretical analysis is verified by simulations.

Computation-efficient solution for fully-connected active noise control window: Analysis and implementation of multichannel adjoint least mean square algorithm

The multichannel active noise control (MCANC) system, in which multiple reference sensors and actuators are used to enlarge the noise-cancellation zone, is widely utilized in complex acoustic environments. However, as the number of channels increases, the practicality decreases due to the exponential rise in computational complexity. This paper, therefore, revisits the adjoint least mean square (ALMS) algorithm and its multichannel applications. The computational analysis reveals that the multichannel adjoint least mean square (McALMS) algorithm11The code is shared on https://www.mathworks.com/matlabcentral/fileexchange/127554-multichannel-adjoint-lms-algorithm-for-multichannel-anc. has a significantly lower computation cost when implementing the fully connected active noise control (ANC) structure. In addition to this advantage, the theoretical analysis presented in this paper demonstrates that the McALMS algorithm can achieve the same optimal solution as the standard adaptive algorithm without the assumptions of input independence and white Gaussian noise. In addition, a practical step-size estimation strategy based on the Golden-section search (GSS) method is proposed to predict the fast step size of the McALMS algorithm. The numerical simulations in a multichannel ANC system demonstrate the effectiveness of the McALMS algorithm and validate the derived theoretical analysis. Furthermore, the McALMS algorithm with proposed step-size approach is used to implement a multichannel noise cancellation window that achieves satisfactory global noise reduction performance for tonal, broadband, and even real-world noises.

Spatially selective active noise control systems.

Active noise control (ANC) systems are commonly designed to achieve maximal sound reduction regardless of the incident direction of the sound. When desired sound is present, the state-of-the-art methods add a separate system to reconstruct it. This can result in distortion and latency. In this work, we propose a multi-channel ANC system that only reduces sound from undesired directions, and the system truly preserves the desired sound instead of reproducing it. The proposed algorithm imposes a spatial constraint on the hybrid ANC cost function to achieve spatial selectivity. Based on a six-channel microphone array on a pair of augmented eyeglasses, results show that the system minimized only noise coming from undesired directions. The control performance could be maintained even when the array was heavily perturbed. The proposed algorithm was also compared with the existing methods in the literature. Not only did the proposed system provide better noise reduction, but it also required much less effort. The binaural localization cues did not need to be reconstructed since the system preserved the physical sound wave from the desired source.

Mitigating wind noise in multichannel active noise control systems

Multichannel active noise control (ANC) systems have been investigated to reduce outdoor low-frequency noise, such as power transformer noise and traffic noise. However, in outdoor environments, wind noise is inevitable and significantly degrades the noise reduction performance of active control systems. Despite broad adoption in windy environments, porous microphone windscreens cannot eliminate low-frequency wind noise in microphones, especially when the wind speed is high. Assuming wind noise is additive and uncorrelated with the primary noise to be controlled, this paper analyses how the wind noise detected by the reference microphone impacts the control filters and the residual noise of a multichannel ANC system based on the multichannel filtered-reference least mean squares algorithm (MFxLMS). By extending our recent research on wind noise suppression in single-channel active noise control systems [Y. Chu, et al., J. Acoust. Soc. Am. 152(6), 3340–3345 (2022)], a modified MFxLMS algorithm based on a new cost function is proposed. Simulations with real-recorded wind noise are performed to verify the theoretical analysis.

Experimental tests of a multichannel active noise control system with single and multiple reference signals applied to the cabin of a tractor

A feedforward active noise control system represents a valid solution whenever it is necessary to attenuate a disturbing noise at low frequency. One example of application is the case of tractors, in which the agricultural operators are exposed for several hours per day to loud noise at low frequency, mainly generated by the engine. Generally, the feedforward systems make use of one reference sensor to measure the noise produced by the main source. However, this way of proceeding could be inadequate to describe the disturbing noise, especially when the noise is divided both in structure-borne and air-borne components. In this article, a comparison between the use of a single and multiple reference signals, processed in a multichannel active noise control system applied to the cabin of a motionless tractor, is proposed. In particular, an accelerometer positioned on the turbocharger is used to sense the vibration generated by the engine, while a microphone placed near the engine is used to measure the air-borne noise. The active system implements the filtered-X least mean squares (FXLMS) algorithm to produce the anti-noise signals. The results show that the use of two simultaneously acquired reference signals improves the performance of the active system.

Multichannel two-gradient direction filtered reference least mean square algorithm for output-constrained multichannel active noise control

Multichannel active noise control is a proven and long-standing technique for achieving a large zone of quiet in enclosures and open spaces. In practice, the multichannel active noise control system encounters more significant output saturation nonlinearity than the single-channel system as a result of the increased number of multiple output amplifiers, which not only degrades noise reduction performance but also compromises system stability. Conventional output-saturation solutions are mainly designed for single-channel applications, and their high computational complexity prevents them from being extended to multichannel systems. Hence, this paper proposes a novel two-gradient direction multichannel filtered reference least mean square (FxLMS) method that effectively avoids the saturation problem by restricting the output to the desired range and marginally increasing the computation over the conventional Multichannel FxLMS algorithm. In addition, this paper investigates the step sizes of the algorithm and proposes a computation-efficient variable step-size strategy to reduce further the steady-state error caused by the varying gradient directions. Finally, the efficacy of the proposed algorithm is demonstrated through numerical simulations using measured primary and secondary paths.

Distributed Active Noise Control Based on an Augmented Diffusion FxLMS Algorithm

Multichannel active noise control (ANC) systems have been widely investigated for low-frequency noise attenuation over a spatial region. Using a conventional centralized control strategy based on the multichannel filtered- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> least mean square (F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS) algorithm has been demonstrated to be effective for multichannel ANC systems, but the high computational burden restricts its practical applications. Meanwhile, a decentralized control strategy suffers from stability problems although it has been successful in reducing the computational load. Recently, distributed control strategies, such as the multitask diffusion adaptation scheme, have been introduced to ANC systems and shown to mitigate the stability problems in decentralized systems. However, distributed ANC systems using the diffusion F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS algorithm require strong symmetry of acoustic paths because of the dependence on node-based adaptation and neighborhood-based combination. To overcome this limitation, this paper proposes an Augmented Diffusion F <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> LMS algorithm with neighborhood-based adaptation and node-based combination. A theoretical formulation and convergence analysis are presented and simulations are performed to compare the proposed algorithm with existing ones under different system configurations for tonal, multi-tonal, narrowband and broadband signals. Simulation results demonstrate that the proposed algorithm has the same noise reduction performance as centralized method even if the acoustic paths are strongly asymmetrical, which is superior over existing distributed multitask diffusion strategy.

A Decoupling Algorithm Based on PID Neural Network for Multi-Channel Active Noise Control of Nonstationary Noise

It is critical to study multi-channel active noise control (ANC) systems to satisfy the requirements of noise reduction in multi-target positions. However, the complexity of the system may increase as the result of an increased number of sound channels. In addition, multi-channel coupling affects the stability of a system. In this paper, a decoupling algorithm based on the Proportion Integration Differentiation (PID) neural network and the filtered-x least-mean-square (FxLMS) for the multi-channel ANC of nonstationary noise is proposed. Due to the nonlinear characteristics of the PID neural network, the coupling problem can be solved through the algorithm. The performance of the novel proposed algorithm is verified by comparing the simulation results with the results from the traditional FxLMS algorithm and the FxLMS algorithm with matrix decoupling. The results illustrate that the convergence speed of the traditional FxLMS algorithm is similar to that of the FxLMS algorithm with matrix decoupling, while the proposed algorithm converges significantly faster than the other two algorithms. In terms of control performance, the proposed algorithm executes the best and the residual error signal has the minimum amplitude, followed by the FxLMS algorithm with matrix decoupling and the traditional FxLMS algorithm. With the advantages in convergence speed and control performance, the proposed decoupling algorithm could be suitable for multi-channel nonstationary ANC.

Robust parallel virtual sensing method for feedback active noise control in a headrest

Additional filter method (AFM) and remote microphone method (RMM) are the two common virtual sensing methods used to compensate for active noise control (ANC) in headrest systems where mounting the error microphone at the desired zone is inconvenient. AFM and RMM are divided into two stages: the training and control stages. In the training stage, a temporary microphone is installed at the target to generate the additional and observation filters for AFM and RMM, respectively. However, they are fully effective only when the disturbances are consistent during both the training and control stages. To solve this dilemma, we propose a robust parallel virtual sensing method (PVSM) for multichannel adaptive feedback ANC system to attenuate the varying narrowband noises. During the training stage, the potential primary noises are used to model a group of parallel additional filters. Subsequently, the synthetic observation filter required for estimating the virtual error signal is derived using minimax optimisation. In the control stage, the most eligible additional filter that minimises the energy of the estimated virtual error signal is matched per frame based on the minimum-mean-square-estimated-error-matching mechanism and applied to the adaptive controller. Theoretical analysis validates that when the primary source varies, PVSM is more robust than both AFM and RMM, and performs noise reduction similar to the direct control with the temporary error microphone placed at the target. The proposed PVSM is further analysed in terms of its convergence condition and computational complexity. Numerous simulations and real-time experiments conducted using a dual-channel ANC headrest validate the feasibility of the proposed algorithm and present a guideline for the selection of frame length in PVSM. Therefore, the proposed PVSM can be considered a robust virtual sensing method against varying primary disturbances.

Some Practical Acoustic Design and Typical Control Strategies for Multichannel Active Noise Control

Active noise control (ANC) systems usually involve a large number of loudspeakers and error microphones in order to achieve noise reduction over an extended region of space. Although fundamentals of ANC theory and principles of ANC methods have been well-established over the past 40 years, applications of this technology are facing new challenges. A larger quiet zone with better noise reduction performance is always desirable in a variety of real-life scenarios. This paper presents several important factors that affect the performance of multichannel ANC systems in some popular applications such as windows with natural ventilation and quiet-zone around heads. The factors affecting acoustic design include the reflection of a baffle plate, arrangement of error sensors in open areas, and so on. In addition, different control strategies are compared and analyzed, including centralized, decentralized, and distributed strategies. All these strategies are discussed from the signal processing side, which should be considered after a proper acoustic design. One of the important aims of this paper is to provide practical guidance for acoustic design and discuss several typical control strategies for multichannel ANC systems.

Singular vector filtering method for mitigation of disturbance enhancement in multichannel active noise control systems

In the design of multichannel active noise control filters, the disturbance enhancement phenomenon will sometimes occur, i.e., the resulting sound is enhanced instead of being reduced in some frequency bands, if the control filter is designed to minimize the power of error signals in other frequency bands or across all frequencies. In previous work, a truncated singular value decomposition method was applied to the system autocorrelation matrix to mitigate the disturbance enhancement. Some small singular values and the associated singular vectors are removed, if they are responsible for unwanted disturbance enhancement in some frequency bands. However, some of these removed singular vectors may still contribute to the noise control performance in other frequency bands; thus, a direct truncation will degrade the noise control performance. In the present work, through an additional filtering process, the set of singular vectors that causes the disturbance enhancement is replaced by a set of new singular vectors whose frequency responses are attenuated in the frequency band where disturbance enhancement occurs, while the frequency responses in other frequency bands are unchanged. Compared with truncation approach, the proposed method can maintain the performance in the noise reduction bands, while mitigating the influence in disturbance enhancement bands.

Multi-channel feedforward active noise control system for reducing snore noise with snore noise-term detection

In this paper, a multi-channel feedforward active noise control system for reducing snore noise with noise-term detection is proposed. The snore noise consists of a noise-term and a silent-term, and it is difficult to reduce the snore noise by the active noise control system. Since the conventional multi-channel feedforward active noise control system updates the noise control filters even in the silent-term, the conventional active noise control system updates the noise control filters unnecessarily. Therefore, the proposed multi-channel feedforward active noise control system introduces threshold processing to update the noise control filters only in the noise-term. Owing to this process, it is possible to reduce the update count of the noise control filters. Simulation results show that the proposed active noise control system can reduce the snore noise as same as the conventional active noise control system and can reduce the update count of the noise control filters compared to the conventional active noise control system.

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.