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Constrained by the computing power, adaptive active noise control systems often have a low sampling rate. Therefore, reconstruction filters and anti-aliasing filters with fixed parameters are generally adopted to eliminate the mirror noise and aliasing noise, respectively; however, they may boost the group delay of the system. A dynamic matching method based on dual sampling rates is proposed to dynamically adjust the parameters of the reconstruction and anti-aliasing filters, according to the characteristics of the primary sound source, for a compromise between high-frequency noise and group delay. In digital high-sampling-rate regions, data that include high-frequency information are analyzed regularly, following which the parameters of the reconstruction filters and those of the anti-aliasing filters are dynamically matched. In digital low-sampling-rate regions, the estimation of the secondary path transfer function is updated. The results of laboratory experiments show that the proposed method not only can suppress the mirror and aliasing noise for primary sound sources with different spectra, but can also effectively reduce the group delay and improve the noise reduction performance of a system.

When the adaptive filter length is increased, the calculation complexity increases rapidly because the relationship between the calculation and the adaptive filter length N contains a power function with no secondary path identification algorithm. Under the basic premise of unreduced noise reduction, herein, a simplified frequency-domain feedback active noise control algorithm is proposed. To reduce the computation complexity, the total delay is adopted as the estimated secondary path; the filtered reference signal is produced in the frequency domain by using multiplication to replace convolution calculation in the time domain and then updating the adaptive filter coefficients in the frequency domain. Therefore, the computational complexity has a logarithmic function with the increased adaptive filter length in the proposed algorithm. If the adaptive filter length is 512, the existing WSMANC algorithm’s calculation is 271,360 real number multiplications, while that of the proposed algorithm is only 38,912 real number multiplications. To verify the proposed algorithm’s stability, convergence speed, and noise reduction, the single-frequency noise, narrowband white noise, and narrowband pink noise, respectively, are used as the primary noise types in the simulations. The results show that (1) the proposed SFDFBANC algorithm can obtain similar noise reduction performance to existing algorithm, (2) the convergence rate is faster than existing algorithm, and (3) if the adaptive filter length is more than 64, the proposed algorithm exhibits a lower computational complexity.

The uncertainty in the secondary path of active noise control (ANC) headphones affects the waterbed effect and stability of the feedback system. This study focuses on the uncertainty of the secondary path when real users wear headphones and proposes a new uncertainty constraint based on the measured results of the secondary path transfer function under different wearing conditions of a dummy head and limited subjects. This constraint and a cascaded second-order infinite impulse response filter with fixed coefficients are used to formulate a control strategic function, which is optimized using the Improved Grey Wolf Optimizer (IGWO) algorithm to obtain the optimal controller with better noise reduction performance. The proposed method and simulation model are validated based on the experimental test results. The results demonstrate that the safety factor and waterbed suppressing factor contained in the proposed uncertainty constraint ensure more stable noise reduction and effective suppression of the waterbed effect for new subjects without a priori data.

As urbanization accelerates, the increasing number of vehicles and travel demands contribute to escalating road traffic noise pollution. Although passive noise control techniques such as noise barriers and green belts effectively mitigate noise, they occupy urban space, exacerbating the scarcity and high cost of already congested city areas. Emerging as a novel noise reduction strategy, active noise control (ANC) eliminates the need for physical isolation structures and addresses the noise within specific frequency ranges more effectively. This paper investigates the characteristics of urban road traffic noise and develops an ANC prototype. Utilizing the Least Mean Squares (LMS) algorithm, we conduct active noise control tests for various types of single- and dual-frequency noise within the prototype’s universal platform to validate its actual noise reduction capabilities. The study demonstrates that urban road traffic noise is mostly in the mid- to low-frequency range (below 2000 Hz). The developed ANC prototype significantly reduces single- or dual-frequency noise within this range, achieving a maximum noise reduction of nearly 30 dB(A). Future research should expand noise reduction tests across more frequency bands and assess the noise reduction effectiveness against real road traffic noise.

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.

In this paper, a phase-scheduled-command filtered-x least mean square (FXLMS) algorithm with a phase error between the disturbance and command signal is analyzed in detail. The influence of the phase error on the control effort, convergence time constant and performance of convergence is explained for both stationary and nonstationary disturbance signal cases. An estimation ratio, in both stationary and nonstationary disturbance cases, of the pseudo-error MSE convergence performance is also developed and discussed. Simulation results were collected to verify the analysis. The results show that, for both stationary and nonstationary disturbance, the phase error may heavily increase the distance of the optimum vector from the initial value, leading to a large control effort, a large convergence time constant and poor convergence performance. The estimation ratio also has a satisfactory performance for estimating the influence of the phase error.

Recent research shows that a directional quiet zone from near to far field can be created with a near-field microphone array. However, there is no systematic theoretical analysis of this error-sensing strategy. In this paper, an analytical model of the angular spectrum of point sources in the free field is proposed, based on which the near-field error-sensing strategy for active directivity noise control can be quantitively analyzed. It is found that the noise reduction in the far field is determined by the angular spectrum estimation error of both the primary and secondary sources. The impacts of the error microphone array configuration, sound source position, and target direction on the estimation error of angular spectrum are investigated. Finally, experiments were conducted in the anechoic chamber to validate the impact of the location of the secondary source.

Monotone noises at high decibels have been identified as a main cause of degradation in people’s mental health. This paper proposes a directional monotone noise reduction method to suppress spatially localized single-frequency noises. The system is designed based on a feedforward active noise control (ANC) structure by implementing filtered-x least mean square (FxLMS) algorithms. Compared with traditional ANC methods, our system employs a parametric array loudspeaker (PAL) as the active noise-canceling source with high audio directivity for directional noise suppression. The system monitors the ambient monotone noise and implements the ANC algorithm in real-time through a software-based platform operating on a generic personal computer (PC). Experimental measurements demonstrate an 8dB reduction of different monotone noises at a 260cm distance from the active source. Compared with traditional ANC methods with a voice coil loudspeaker (VCL) as the noise-canceling source, our PAL-based system achieves similar noise suppression performance with a 5.8 times improvement in the source-to-target distance and 64% reduction in the −3dB audio main lobe beam width. The results prove the advantage of introducing PALs as active-noise-canceling sources for monotone noise suppression with a cost-effective enhancement in operating distances and noise control directivities.

Diversified noise sources pose great challenges in the engineering of an ANC (active noise control) system design. To solve this problem, this paper proposes an ANC method based on VMD (variational mode decomposition) and Ensemble Learning. VMD is used to extract IMFs (Intrinsic Model Functions) of different types of noise and obtain the approximate entropy of each IMF. Clustering analysis on the output of VMD is conducted based on the PCA (principal component analysis) dimension reduction method and k-means++ method to get classification results for different noises. On the basis of the clustering results, different GBDT (gradient boosting decision tree) regressors are constructed for different noise types, in order to create a high-performance ANC system for multiple noise sources. To verify the effectiveness of the proposed method, this paper designed four simulation schemes for the ANC: obstacle-free rectangular enclosed space, rectangular enclosed space with obstacle, obstacle-free trapezoidal enclosed space and trapezoidal enclosed space with obstacle. When machine gun noise is used as an example, noise attenuation by the proposed method in four simulation schemes is −23.27 dB, −21.6 dB, −19.08 dB and −15.48 dB respectively.

It is well-known that performance of the classical algorithms for active noise control (ANC) systems severely degrades when implemented for controlling the impulsive sources. The objective of this paper is to propose a new recursive least squares (RLS) algorithm (and its variant) for being implemented in the framework of ANC systems. The proposed RLS-based adaptive algorithm employs an objective function designed to achieve robustness against the impulse type sources. The derivation of the algorithm is quite straightforward; however, a few modifications have been incorporated to address the application at hand. In order to improve upon the numerical stability issue of RLS-based adaptation, it is suggested to employ smoothing while updating the inverse correlation matrix. Furthermore, it is proposed to introduce a step size in the update equation of the adaptive algorithm. This results in the fixed step-size modified filtered-x (MFx) robust RLS (FSS-MFxRRLS) algorithm. As expected, a fixed value step size results in a trade-off situation for convergence speed and steady-state misalignment. In order to address this issue of a trade-off situation, the idea of a convex combined step size (CCSS) is introduced into the adaptive procedure to develop the CCSS-MFxRRLS algorithm. When the ANC is started, the CCSS strategy (automatically) selects a large-valued step size to achieve a fast initial convergence. As the ANC system converges at the steady-state, the CCSS is automatically tuned to a small value which improves the steady-state performance of the proposed CCSS-MFxRRLS algorithm. Extensive simulations have been designed to mimic many scenarios for practical applications of ANC for impulsive sources. The simulation results demonstrate that the proposed CCSS-MFxRRLS algorithm is very effective in many practical scenarios involving ANC of impulsive sources.

NVH (noise, vibration, and harshness) is a key factor affecting vehicle comfort. Compared with traditional sound absorption and isolation methods, active noise control (ANC) offers a significant advantage in solving the problem of low- and medium-frequency noise from road surfaces. However, the classic filtered-x least mean squares (FxLMS) algorithm is ineffective in terms of adapting to different road noises to ensure a stable noise reduction effect when facing the complex and changeable noise environment of moving vehicles. Therefore, an adaptive step size algorithm (ASSFxLMS) is proposed in this paper, which can adjust the step size according to the size of the reference signal to ensure the stability of the adaptive process. In order to improve the performance of the algorithm, a particle swarm optimization algorithm is also used to automatically adjust the parameters, so that the step size of the adaptive algorithm always maintains a relatively ideal size. The simulated pulse noise of standard SαS distribution was used as the reference signal for the simulation. The simulation results show that compared with other algorithms, the proposed algorithm under different degrees of pulse noise conditions, noise reduction stability, and noise reduction amplitude are improved. In order to further verify the feasibility of the algorithm in vehicle road noise reduction, this paper also conducted a hardware-in-the-loop noise reduction experiment in the laboratory, employing the road noise data collected by the real vehicle. Under different interior noise conditions, the proposed active noise-control system has a maximum noise reduction effect of 12 dB for low-frequency noise below 100 Hz.

The movement and rotation of the human head can significantly degrade the control performance of active headrests with virtual microphones. This paper investigates the performance robustness of an active headrest with virtual microphones against the head rotation in a pure tone diffracted diffuse sound field. A physical model of a headrest system with a diffractive sphere is firstly developed, based on which the influence of acoustic transfer responses on the performance robustness against head rotation is analyzed. Then, the multi-objective optimization with constraints is developed to improve the performance robustness of the system, by designing optimized plant responses. Simulation results show that the method is effective in improving the robustness of single and dual channel systems. For a dual-channel headrest system with the proposed arrangements of secondary sources and microphones, the promotion of the minimum noise reduction is more than 7 dB after optimization when the head rotates from −90 degrees to 90 degrees at 125 Hz, 250 Hz and 500 Hz. In addition, the influence of different parameters on the optimization method is discussed for this dual-channel system. Finally, experiments carried out in an ordinary room validate the effectiveness of the proposed method.

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.

In this paper, we propose horizontal active noise control (ANC) using two-dimensional wave field information alone. By reducing the control space to a horizontal plane, the number of microphones and speakers was considerably reduced compared with ANC systems using three-dimensional wave field information. The radii of the reference, microphone, and loudspeaker array were determined based on the wave field reproduction error. Accordingly, the simulation and experimental results of the proposed ANC system were presented based on the use of five microphones and loudspeakers using conventional ANC algorithms. Overall, an average noise reduction of 20 dB was observed inside the microphone array with a radius of 0.5 m for tonal noise at 200 Hz. This performance is acceptable with a drastically reduced number of microphones and speakers. The findings of this study, along with further research conducted in a reverberant room, represent a significant contribution to global ANC commercialization.

Environmental noise causes enormous harm to human health. This research suggests an active noise control (ANC) system based on the cuckoo search (CS) algorithm to reduce indoor noise pollution. The indoor environment’s ANC is more complicated than the conventional linear ANC system used in cars, aircraft, and other confined spaces. The suggested system architecture considers noise from many directions in order to reduce the impact of external noise on persons within a given environment. The effective search strategy of the CS algorithm enhances the approach of updating filter coefficients based on the LMS/NLMS algorithm in the conventional ANC systems. The results from the simulations demonstrate that the suggested strategy successfully validates the hypothesis and provides significant noise reduction. Additionally, we developed the system’s hardware, which is based on a digital signal processor (DSP). The experimental results show that the proposed technology could perform well with respect to ANC.

In this paper, active control of flow-induced cavity resonance noise is addressed. A hybrid numerical method is presented to predict the resonance frequency and, instead of traditional active flow control, a narrowband active noise control system is utilized to suppress the resonance. A duct system is built up at low Mach numbers and experiments are carried out to validate the proposed methods. The results have shown that the resonance frequency could be predicted with 1.5% errors and the flow-induced narrowband noise could be effectively suppressed at both the fundamental frequency and its first harmonic. More than 10 dB global attenuations could be achieved for the fundamental resonance frequency without noise enhancements at other frequencies. Further, it was also found that the optimal reference frequency of the narrowband active noise control system could be largely biased from the original resonance frequency, which indicates a nonlinear mechanism of the control system.

Sound field control (SFC) technology enables the active management of audio delivered within an acoustical environment. It includes three research directions: sound field reproduction, personal audio systems, and active noise control. Sound field reproduction uses loudspeaker arrays to replicate a sound field in a target region; personal audio systems extend sound field reproduction over multiple regions so that different listeners can hear personalized audio in a shared space; and active noise control aims to cancel the original sound field in the target area by generating a secondary sound field. In this paper, we briefly review the advances of the three different types of techniques with a discussion of their algorithms and applications.

In turboprop aircraft, the low-frequency noise field created by the propellers is the major contributor to the interior vibro-acoustic field, which determines a passenger’s discomfort. This paper deals with the experimental assessment of an active noise control (ANC) system for cabin seat headrests using two loudspeakers placed on both sides of the passenger’s head to create a local zone of quiet around the passenger’s ears. To deal with time-varying disturbances, the developed ANC system utilized a two-input-two-output filtered-X LMS algorithm developed in MATLAB/Simulink and implemented on a DSPACE control board to drive the secondary speakers and cancel the unwanted low-frequency noise components. The performance of the active headrest was investigated through real-time experimentation involving sensors, actuators, and electronic devices. The test results showed that up to approximatively 20 dB of sound attenuation could be realized in the passenger’s ears over a narrowband sound field replicated under laboratory conditions. Such achievements represent an excellent starting point toward practical applications in the design of more comfortable and acoustically efficient aircraft cabin seats.

Due to the complexity of the coupling between the active noise control (ANC) controller and secondary path estimator, performance analysis of the system becomes particularly difficult. At present, the performance analysis of the system is often based on the fact that the secondary path tends to be stable, and the secondary path fitting error is minimal. However, in the early stage of system operation, or when the secondary path changes suddenly, the secondary path fitting error is significant, which easily causes divergence of the system control. It is still unable to guarantee the step-size bounds of convergence stability. Therefore, factor decomposition was used to analyze the mean weight behavior in this study. This strategy emphasizes the influence of secondary path modeling (SPM) error. The mean square behavior was evaluated using the energy conservation relationship. According to the established theoretical model, the convergence condition of the system was derived and the upper bound of step size suitable for all stages of system operation was obtained. The simulation and experimental results show that the ANC system is quite stable and robust under extreme conditions and has an obvious noise reduction effect in a specific range of open space, which can reach about 20 dB noise reduction.

This paper presents an investigation on the feedback controller design for active noise control headphones under the condition that the frequency responses of the primary and secondary paths corresponding to the feedback microphone do not match to the ones corresponding to the human ear. The influence of such mismatches on the performance are analyzed first, and then an optimization method is proposed to enhance the comprehensive performance at the human ear. In the proposed method, the feedback loop is constructed directly with the feedback microphone and any extra filters of the virtual sensing techniques are avoided. Cascade biquad filters are used as the controller, which is in accordance with current applications. A differential evolution algorithm was used to solve the proposed optimization problem, and the optimal parameters of the controller were found. It has been shown by the experimental results that, at the dummy head ear position, good noise reduction performance could be obtained at the low frequency band with limited noise enhancement for high frequencies, even if large frequency response mismatches exist.

Nonlinear active control is very important in many practical applications. Many well-known nonlinear active noise control algorithms may suffer from high computational complexity and low convergence speed, especially in the nonlinear secondary path case. Thus, it is still an actively researched topic for reducing complexity and improving the convergence rate. This paper presents a low-complexity Volterra filtered-error least mean square algorithm when taking a decomposable Volterra model into account for active control of nonlinear noise processes, which is referred as DVMFELMS. The computational complexity analysis shows that the proposed DVMFELMS algorithm can significantly reduce the nonlinear active noise control system’s complexity. The simulation results further show that the proposed algorithm can achieve promising performance compared with the Volterra-based FELMS algorithm and other state-of-the-art nonlinear filters, while the decomposable error of the Volterra kernel may be introduced inevitably. Moreover, the proposed DVMFELMS algorithm shows a better convergence rate in the broadband primary noise case due to fewer parameters used in each sub-filter.

Active noise control can be used to reduce the scattered sound of a reflecting object to make it invisible to incident acoustic waves. For the multi-zone active noise control of scattered sound from an infinite rigid cylinder, an active control strategy is proposed that combines the least absolute shrinkage and selection operator (LASSO) algorithm with constraint points and regularized least squares (RLS) algorithm. The proposed control strategy is used to promote control performance through optimizing the secondary loudspeaker placement of an active noise control system. Compared with the RLS algorithm employing the uniformly placed loudspeakers and the traditional LASSO algorithm, the proposed strategy has better reduction performance both in the forward-scattered and backward-scattered sound target areas, and there is less sound amplification in the far field. From 400 Hz–1100 Hz, the proposed strategy provides a 5 dB–16 dB reduction performance advantage in the target area compared to the RLS algorithm employing uniformly placed loudspeakers.

Partial updates (PU) of adaptive filters have been successfully applied in different contexts to lower the computational costs of many control systems. In a PU adaptive algorithm, only a fraction of the coefficients is updated per iteration. Particularly, this idea has been proved as a valid strategy in the active control of periodic noise consisting of a sum of harmonics. The convergence analysis carried out here is based on the periodic nature of the input signal, which makes it possible to formulate the adaptive process with a matrix-based approach, the periodic least-mean-square (P-LMS) algorithm In this paper, we obtain the upper bound that limits the step-size parameter of the sequential PU P-LMS algorithm and compare it to the bound of the full-update P-LMS algorithm. Thus, the limiting value for the step-size parameter is expressed in terms of the step-size gain of the PU algorithm. This gain in step-size is the quotient between the upper bounds ensuring convergence in the following two scenarios: first, when PU are carried out and, second, when every coefficient is updated during every cycle. This step-size gain gives the factor by which the step-size can be multiplied so as to compensate for the convergence speed reduction of the sequential PU algorithm, which is an inherently slower strategy. Results are compared with previous results based on the standard sequential PU LMS formulation. Frequency-dependent notches in the step-size gain are not present with the matrix-based formulation of the P-LMS. Simulated results confirm the expected behavior.

Active headrest is an essential application for active noise control, capable of reducing low-frequency disturbance around an error microphone. However, in most cases, attaching microphones to ears is not feasible and noise attenuation performance is vulnerable to plant response variations. This paper presents a multichannel feedback active headrest system combined with the virtual microphone method and a manually adjustable headrest structure for users. Applying the virtual microphone method can transfer the attenuation target from the distant microphone to the ear. The proposed adjustable headrest structure allows for secondary loudspeakers and the corresponding microphone to be moved as a single unit while maintaining the relative distances between the secondary loudspeakers and microphones constant to ensure that the related plant responses are consistent. Experiments were conducted to validate the performance against multi-sinusoidal machine noise. The results demonstrate the benefits of the proposed structure over conventional structures. Additionally, the applicability of the three commonly used virtual sensing methods (the auxiliary filter method, remote microphone method, and virtual microphone method) in various practical conditions was verified when using the proposed adjustable headrest structure. Furthermore, 10 volunteers were involved in the evaluation, and the robustness of the proposed system for various users was confirmed.

This work deals with the reduction of aircraft interior noise using active sidewall panels (linings). Research work done in the past showed that considerable reductions of the sound pressure level (SPL) in the cabin are possible using structural actuators mounted on the lining and error microphones distributed in front of the linings. However, microphones are undesirable for error sensing because they are not suitable for the realisation of an integrated and autonomous active lining (smart lining module). Therefore, the goal of the present work is the replacement of the microphones by structural sensors. Using the structural sensors as remote sensors in combination with an acoustic filter, virtual microphones can be defined. The present study relies on experimental data of a double-walled fuselage system which is mounted in a sound transmission loss facility. Simulation results based on measured time data and identified frequency response functions are provided. Different configurations of virtual microphones are investigated regarding the SPL reduction and the induced vibration of the lining panel.

The push for greater urban sustainability has increased the urgency of the search for noise mitigation solutions that allow for natural ventilation into buildings. Although a viable active noise control (ANC) solution with up to 10 dB of global attenuation between 100 Hz and 1000 Hz was previously developed for an open window, it had limited low-frequency performance below 300 Hz, owing to the small loudspeakers used. To improve the low-frequency attenuation, four passive radiator-based speakers were affixed around the opening of a top-hung ventilation window. The active control performance between 100 Hz and 700 Hz on a single top-hung window in a full-sized mock-up apartment room was examined. Active attenuation came close to the performance of the passive insulation provided by fully closing the window for expressway traffic and motorbike passing noise types. For a jet aircraft flyby, the performance of active attenuation with the window fully opened was similar to that of passive insulation with fully closed windows. In the case of low-frequency compressor noise, active attenuation’s performance was significantly better than the passive insulation. Overall, between 8 dB and 12 dB of active attenuation was achieved directly in front of the window opening, and up to 10.5 dB of attenuation was achieved across the entire room.

Barriers are increasingly used to protect the pedestrian and neighboring buildings from construction noise activities. This study aims to investigate the suitability of applying active noise control on barriers in a construction site to protect the street area and neighboring buildings. Transducers that are simulated in this work are close to the barrier, and their optimal positions are defined in such a way that the control system has the maximum performance at the neighboring areas close to the construction sites. To begin with, the suitable location of the control sources is found when the total squared pressure is minimized at the positions of noise receivers. The suitable location of the error sensors is, then, found when the control sources are fixed at the position of the previous step and the total squared pressure is minimized at the error sensors. The best location for the error sensors is defined when the maximum reduction is achieved in the target area. It is observed that suitable positions for the transducers depend on the location of target areas for noise control, the position of the noise source, and its operating frequency. In this investigation, a unique configuration is proposed for the transducers that achieves a comparable reduction both at the street area and the neighboring buildings, simultaneously. The results show that the active noise barrier with a height of 2.5 m can achieve an extra insertion loss in the street zone, varies from 9.3 to 16.4 dB (in comparison with passive noise barrier) when the distance of the noise source from the barrier changes in the range of 7 to 1 m, respectively. Those values are of the same order for the passive noise attenuation. Furthermore, similar results are achieved when attempting to cancel the shadow zone of a façade 15 m away from the barrier.

The idea of active noise control is an attenuation of unwanted noise with an additionally generated acoustic wave using the phenomenon of interference. Its technical realization employs advanced control algorithms. Active noise control is an area of intense research and practical engineering applications. In the paper a new structure of adaptive active noise control systems is proposed. Compared with classical control systems used for active noise control, the proposed structure contains in an error signal measurement path an additional discrete-time filter that estimates signal values at the input of this path. These estimates are then used to tune the corresponding adaptive filter. Properties of the proposed adaptive active noise control structure are illustrated by simulation examples in which a feedforward control system equipped with this additional filter is used to attenuate unwanted wide-sense stationary random noises with continuous and mixed spectra.

In this study, we conducted a quantitative evaluation of the comfort of the interior of an electric vehicle (EV) using the brain wave appearance rate, which is part of the human biologic information that the initial stage of the proposed active noise control (ANC) system for ultra-compact EVs reveals. EVs have become easy-to-use mobility solutions and have been researched and actively developed focusing on using music characteristics. We performed fundamental testing of music including 1/f fluctuation for the evaluation of ride comfort based on the relationship between the participant’s heart rate and tempo of music using a driving simulator. The results suggest that if a passenger listened to music including a 1/f fluctuation, then he/she could relax. Thus, it was concluded that if we could pre-grasp the passenger’s biologic information of the heart rate and beats per minute for masking, then the comfort in the interior space could be improved even when using a driving simulator.

Reduction of noise generated by devices is an important problem, both in industrial environments where high sound pressure levels may damage hearing, and in households where the sound pressure level is usually moderate, but may cause discomfort and stress. Classically used passive methods often have poor performance for low frequencies. Alternatively, active control can be used to improve noise reduction in this frequency range. In the proposed approach, noise generated by devices may be reduced by controlling vibrations of the casing. The authors previously confirmed the performance of the proposed active control approach using a dedicated noise-canceling casing. Herein, we describe further development and application of the method to an off-the-shelf washing machine. Electrodynamic actuators were installed on four walls of the washing machine. The performance of the control systems was experimentally evaluated during the real spinning phase and the results are reported here.

Affine projection (AP) algorithms have been demonstrated to have faster convergence speeds than the conventional least mean square (LMS) algorithms. However, LMS algorithms exhibit smaller steady-state mean square errors (MSEs) when compared with affine projection (AP) algorithms. Recently, several authors have proposed alternative methods based on convex combinations to improve the steady-state MSE of AP algorithms, even with the increased computational cost from the simultaneous use of two filters. In this paper, we present an alternative method based on an affine projection-like (APL-I) algorithm and least mean square (LMS) algorithm to solve the ANC under stationary Gaussian noise environments. In particular, we propose a switching filter selection criteria to improve the steady-state MSE without increasing the computational cost when compared with existing models. Here, we validate the proposed strategy in a single and a multichannel system, with and without automatically adjusting the scaling factor of the APL-I algorithm. The results demonstrate that the proposed scheme exploits the best features of each filter (APL-I and LMS) to guarantee rapid convergence with a low steady-state MSE. Additionally, the proposed approach demands a low computational burden compared with existing convex combination approaches, which will potentially lead to the development of real-time ANC applications.

The traditional mean squared error (MSE) criterion can be formulated as a quadratic function of a vector of control filter coefficients, and it is easy to obtain optimal control filter coefficients. Although the MSE criterion can lead to noise reductions in the control area, an unpredictable directional residual sound field is generated. In this paper, we propose a method for multi-channel active harmonic noise control with a local minimax error criterion based on the Nelder−Mead algorithm, which leads to reductions at all error positions and greater reductions at controllable positions. Directional noise reduction experiments of two areas are presented for two different error criteria at discrete locations in an anechoic chamber. Compared with a system employing the traditional MSE criterion, the results show that an active noise control system with the proposed criterion can achieve extra reductions at specified locations and overall noise reductions at the same time. The present research offers some important insights into directional control.

The presence of control signal feedback to the reference microphone in feedforward active control systems deteriorates the control performance. A four-stage method is proposed in this paper to carry out online feedback path modelling with the control signal. It consists of controller initialization, feedback path modelling using decorrelation filters, active control operation, and feedback path change detection for maintaining the control operation. In contrast to the existing auxiliary noise injection method, the proposed method uses five switches and three thresholds to control and maintain the system stability by avoiding the interference between control operation and feedback path modelling, and adaptive decorrelation filters are used to increase the feedback path modelling performance. Simulation results reveal that the proposed method is capable of tracking feedback path changes without injecting any auxiliary noise and maintaining the noise reduction performance and stability of the system.

Active headrest can reduce the low-frequency noise around ears based on the principle of active noise control. This paper presents a combination of robust algorithm and head-tracking for a feedforward active headrest to reduce the broadband noise for a sleeper on a high-speed train. A robust algorithm based on the feedforward active noise control is proposed to improve the noise control performance during head rotations. The head-tracking system with infrared rangefinders tracks the head position based on the Kalman filter to further improve system performance with head movements. Experiments were conducted on a model of a sleeper on a high-speed train. The experimental results show that the proposed active headrest system effectively controls broadband noise with head movements and rotations.

This paper investigates active broadband noise control inside vehicles with a multichannel controller. The noncausal inversion of a practical nonminimum-phase secondary path is formulated, and its influence on noise-reduction performance is analyzed. Based on multiple coherence between reference signals and undesired noise, a novel formulation for identifying primary paths with correlated excitation signals is presented and a causal optimal controller is proposed. Meanwhile, the proposed controller can be used as an accurate predictor to estimate the maximal achievable noise reduction and provide a reference to improve the control systems. The robustness of the proposed algorithm is examined by varying the uncertainty of primary paths. Finally, the performance of the proposed causal optimal controller is validated using the data measured in a car. The results show that the proposed algorithm outperforms traditional algorithms and achieves a significant broadband noise reduction in time-invariant systems.

Electric appliances used in workplaces and everyday life often generate a low-frequency noise, which affects human body systems. Passive methods employed to reduce noise are not effective at low frequencies. The classical approach to active noise control practically involves the generation of local zones of quiet, whereas at other areas the noise is reinforced. Moreover, it usually requires a large number of secondary sound sources. Hence, an active casing approach has been developed. The active casing panels’ vibrations are controlled to reduce the device noise emission. Efficiency of this method has been previously confirmed by the authors and the results have been reported in multiple journal publications. However, in the previous research experiments, the active casing was placed at a distance from the enclosure walls. In this research, the active casing is located in a corner and such placement is intentionally used to facilitate the active control system’s operation. The noise reduction performance is investigated at multiple configurations, including a range of distances from the corner and different error microphone arrangements. The analysis of both primary and secondary paths is given. Advantages and drawbacks of different active casing configurations are presented and discussed.

Affine projection (AP) algorithms have demonstrated faster convergence speed than conventional least mean square (LMS) algorithms, thus providing an attractive solution in the active noise control (ANC) field. However, the AP algorithms demand high computational cost, restricting their practical use in real-time ANC applications. Recently, a multichannel filtered-x error-coded affine projection-like (FXECAP-L) algorithm with evolving order has been proposed to reduce the computational burden by maintaining the convergence speed of AP algorithms. In order to obtain an efficient and robust FXECAP-L algorithm with evolving order, the scaling factor and encoder resolution need to be adjusted manually, which is a time-consuming and costly effort that must be carried out by expert designers. To reduce these costs and efforts, we introduce, for the first time, a strategy for automatic adjustment of the scaling factor and encoder resolution that benefits the rapid development of practical ANC applications. To demonstrate its practical use, we applied the proposed strategy for controlling the noise in an acoustic duct. The practical results demonstrate the automatic adjustment of the FXECAP-L algorithm which maintains high convergence speed at the expense of a small compromise in terms of processing time.

This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.

This paper presents the development of active noise control (ANC) for light-weight earphones, and proposes using music or natural sound to estimate the critical secondary path model instead of extra random noise. Three types of light-weight ANC earphones including in-ear, earbud, and clip phones are developed. Real-time experiments are conducted to evaluate their performance using the built-in microphone inside KEMAR’s ear and to compare with commercially-available ANC headphones and earphones. Experimental results show that the developed light-weight ANC earphones achieve higher noise reduction than the commercial ANC headphones and earphones, and the in-ear ANC earphone has the best noise reduction performance.

This paper investigates the stability problem of the feedback active noise control (ANC) system, which can be caused by the modeling error of the electro-acoustic path estimation in its feedback mechanism. A stability analysis method is proposed to obtain the stability bound as a form of a closed-form equation in terms of the delay error length of the secondary path, the ANC filter length, and the primary noise frequency. In the proposed method, the system’s open loop magnitude and phase response equations are separately exploited and approximated within the Nyquist stability criterion. The stability bound of the proposed method is verified by comparing both the original Nyquist stability condition and the simulation results.

This work presents a practical active noise control system composed of distributed and collaborative acoustic nodes. To this end, experimental tests have been carried out in a listening room with acoustic nodes equipped with loudspeakers and microphones. The communication among the nodes is simulated by software. We have considered a distributed algorithm based on the Filtered-x Least Mean Square (FxLMS) method that introduces collaboration between nodes following an incremental strategy. For improving the processing efficiency in practical scenarios where data acquisition systems work by blocks of samples, the frequency-domain partitioned block technique has been used. Implementation aspects such as computational complexity, processing time of the network and convergence of the algorithm have been analyzed. Experimental results show that, without constraints in the network communications, the proposed distributed algorithm achieves the same performance as the centralized version. The performance of the proposed algorithm over a network with a given communication delay is also included.