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Sound attenuation along a waveguide is intensively studied for applications ranging from heating and air-conditioning ventilation systems, to aircraft turbofan engines. In particular, the new generation of Ultra-High-By-Pass-Ratio turbofan requires higher attenuation at low frequencies, in less space for liner treatment. This demands to go beyond the classical acoustic liner concepts and overcome their limitations. In this paper, we discuss an unconventional boundary operator, called Advection Boundary Law, which can be artificially synthesized by electroactive means, such as Electroacoustic Resonators. This boundary condition entails nonreciprocal propagation, meanwhile enhancing noise transmission attenuation with respect to purely locally-reacting boundaries, along one sense of propagation. Because of its artificial nature though, its acoustical passivity limits are yet to be defined. A thorough numerical study is provided to assess the performances of the Advection Boundary Law, in absence of mean flow. An experimental test-bench validates the numerical outcomes in terms of passivity limits, non-reciprocal propagation and enhanced isolation with respect to local impedance operators. Guidelines are outlined to properly implement the Advection Boundary Law for optimal noise transmission attenuation. Moreover, the tools and criteria provided here can also be employed for the design and characterization of other innovative liners. • Advection Boundary Law introduced as a special Dirichelet-to-Neumann mapping. • Open field absorption and passivity limits. • Duct modes and scattering simulations. • Criteria for passivity and attenuation performances. • Experimental implementation validates the numerical outcomes. [ABSTRACT FROM AUTHOR]

Active noise control (ANC) is an effective method to suppress vehicle road noise. The rapid selection of the reference sensor set and rational selection of its layout position are crucial for determining the vibration noise transmission paths with high contributions, which determines the noise reduction performance of the active road noise control (ARNC) system. The reference sensor set selected by the conventional multiple coherence function (MCOH) method does not always ensure the optimal noise reduction performance and the matrix singularity problem based on the Fisher information matrix (FIM) expansion method. To alleviate this problem, this paper proposes a method based on the overall level of noise reduction (OANR) and the FIM to select a set of reference sensors and determine their positions. This method uses an optimal causally constrained Wiener filter to estimate the theoretical OANR for each control area to ensure the reference sensor selected has the optimal noise reduction performance, and it expands efficiently based on the FIM method to select a set of reference sensors. Besides, the matrix singularity problems in the FIM-based expansion method are solved using noise frequency summation and matrix operation, which makes the method more universally applicable. Numerical simulations are performed to analyze and evaluate the noise reduction performance and selection efficiency of the proposed method under different conditions. In addition, a series of real vehicle ANC experiments are conducted. The results show that the reference sensor set selected based on the proposed method has an obvious advantage in noise reduction performance, and only four reference sensors are needed to achieve 4.63 dB(A) noise reduction in ARNC experiments. It fully confirms the effectiveness of the proposed method in real vehicle applications. [ABSTRACT FROM AUTHOR]

Traditional active noise control (ANC) algorithms suffer from significant performance degradation when dealing with impulsive noise. Although some robust algorithms based on the maximum correntropy criterion (MCC) have been developed to improve the performance, they still have unsatisfactory noise reduction performance due to the steady-state misalignment. Therefore, we propose a filtered-x affine-projection-like exponential hyperbolic sine (FxAPLEHS) algorithm for feedforward ANC systems, inspired by the fast convergence property of the affine-projection-like (APL) algorithm. Here, the optimal step-size parameter of the proposed algorithm is derived mathematically from the a posteriori error via considering the time-varying step-size bound, which aims to enhance the performance of the algorithm in terms of convergence and steady-state misalignment. Moreover, to further improve the overall performance of the FxAPLEHS algorithm, a combined step-size FxAPLEHS (CSS-FxAPLEHS) algorithm is designed, which constructs a step-size mixing parameter using a modified sigmoidal activation function that is indirectly updated with the objective of minimizing the L 1 -norm of the error. The superiority of the proposed algorithms is verified through numerical simulations in symmetric α -stable (S α S) noise and multi-tone noise mixed with S α S noise environments, respectively. In addition, validation simulations based on a real single-channel duct ANC system model further demonstrate the superiority of the proposed algorithms in terms of convergence speed and noise reduction ratio. • The FxAPLEHS algorithm based ANC technique is proposed. • The CSS-FxAPLEHS algorithm to improve noise reduction performance is also proposed. • The computational complexity analysis of the proposed algorithm is carried out. • The proposed algorithms offer enhanced robust ANC performance. [ABSTRACT FROM AUTHOR]

This paper presents a numerical and experimental investigation of a sampled-data, output-feedback indirect adaptive control algorithm for noise and vibration suppression. This technique, called predictive cost adaptive control (PCAC), requires no prior modeling of either the dynamics or the disturbance. Recursive least squares with variable-rate forgetting based on the F-test is used for online, closed-loop system identification in the presence of harmonic and broadband disturbances. A key feature of PCAC is the ability to operate with output feedback, which is enabled by using the block-observable canonical form of the estimated model. The control input is obtained using the discrete-time, receding-horizon, backward-propagating Riccati equation of the linear–quadratic regulator. Numerical simulations of lumped and distributed models are used to assess the ability of PCAC to reject disturbances without prior modeling of the structural dynamics or the disturbance. Laboratory results from an active noise control experiment indicate that, without utilizing an analytical or prior empirical model, without specifying or determining sensor/actuator locations, and without requiring matched disturbances, PCAC demonstrates the capability to suppress noise and vibration. [ABSTRACT FROM AUTHOR]

Virtual sensing methods (VS) enable an active noise control (ANC) system to achieve noise reduction at a target control point, but impractical to place an error microphone for an extended period of time. This is realized by employing remote monitoring microphones. This paper introduces the relative path based VS (RP-VS) method for feedback ANC systems and compares it to existing VS methods, including the auxiliary filter based VS (AF-VS) method and the remote microphone based VS (RM-VS) method. Theoretical analysis is conducted to describe the impact of acoustic path changes on their noise reduction capabilities. When acoustic paths remain constant, all three VS methods perform similarly. However, they behave differently when acoustic paths change: the RP-VS method aligns with the AF-VS method, while the RM-VS method matches the performance of the fixed-coefficient (FC) filter. Moreover, changes in the primary noise characteristics can be treated equivalently as changes in the secondary paths at a single frequency, but these equivalent changes are inherently uncertain. Simulations and experiments conducted on an active headrest hence demonstrate the robustness of the RP-VS method against changing paths and primary noise characteristics. • A new virtual sensing method for feedback active noise control is proposed. • A unified analysis for three virtual sensing methods, including the proposed one, is presented. • The results contribute new insights into virtual sensing methods for feedback active noise control. • Lab-built active feedback headrest validates the proposed virtual sensing method. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, refracto-vibrometry is proposed for active control of sound radiation through an opening. First, refracto-vibrometry is used to reconstruct sound field in an opening, and a quantitative comparison of sound fields is made with those obtained from a microphone array at 224 locations. Then, the reconstructed sound field by refracto-vibrometry is used for active noise control. Experimental results indicate that using refracto-vibrometry can obtain about 12.9 dB noise reduction at 500 Hz with no increase in noise observed at higher frequencies above 2 kHz. By offering a significantly larger number of error signals without causing obstructions at an opening, refracto-vibrometry outperforms traditional microphone arrays, making it an effective complementary error sensing method for high frequency noise. • Refracto-vibrometry (RV) used to unobtrusively determine sound pressure at an opening. • Strong correlation between RV and microphone array measurements for 224-point survey. • RV data allows for many error signals for effective noise control at the opening. • Optimal noise control needs more than double the error sensors to control sources. • Use RV for high frequency, microphone array for low frequency noise control. [ABSTRACT FROM AUTHOR]

Traditional functional linked neural networks (FLNNs) impose a significant computational burden due to their input expansion, primarily stemming from the utilization of digital filters. This paper presents a Laguerre FLNNs filter for nonlinear active noise control (NANC) systems. By employing the truncated Laguerre series, the presented filter achieves effective approximation of long primary paths with a reduced filter length. Moreover, we develop adaptive algorithms rooted in information-theoretic learning (ITL) within the framework of the Laguerre-FLNNs NANC model. Using the ITL criterions, a Laguerre filtered-s maximum correntropy criterion (LFsMCC) algorithm is derived and a Laguerre filtered-s quantized minimum error entropy criterion (LFsQMEE) algorithm is proposed by minimizing Renyi's quadratic entropy. To reduce the computation cost, an online vector quantization method is utilized to improve the LFsQMEE. This technique selectively quantizes the error vectors, reducing them to a smaller subset of samples within the codebook. Moreover, an enhanced LFsQMEE with a fiducial point is introduced. The steady-state performance and the computational complexity are analyzed. Theoretical analysis is validated through simulations and the control performance of the proposed model and algorithms is tested in experiments with both simulated and real paths. [ABSTRACT FROM AUTHOR]

Multi-channel active noise control (ANC) is widely applied as a practical approach to achieving a large noise cancellation area. However, the efficacy of the multi-channel ANC is significantly impacted by the quality of the reference signal in practice. Hence, we propose using wireless reference microphones in the multi-channel ANC, which substantially improves the reference signal quality and increases the noise reduction performance. Reference signal-to-interference ratio (RSIR) is brought for analysis to understand how the wireless multi-channel ANC system works. According to the theoretical derivation, the multi-channel adjoint least mean square (LMS) algorithm benefits from the higher RSIR that the wireless reference microphone brings about regarding steady-state performance. Furthermore, the numerical simulation and real-time experiments on a multi-channel ANC window in a resident room confirm these theoretical analyses. [ABSTRACT FROM AUTHOR]

• A novel auxiliary active noise control system based on signal reconstruction is proposed. • A signal reconstruction model for multi-networks based on decomposition optimization is design. • The Bat Algorithm is used to optimize the weights of online AANC controller. • Results demonstrate that proposed system can balance modeling efficiency and accuracy. Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design; Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the VSS-LMS algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid analog-digital architecture, aimed at reducing hardware resource consumption and improving the scalability of a multi-channel feedforward active noise control system to accommodate a large number of error microphones. This hybrid architecture employs a partial-update filtered-x least mean squares algorithm that is lightweight and suitable for real-time execution, as it updates the control filters in each iteration using only two selected error signals. The selection of these two error signals is achieved through a dedicated analog circuit comprising analog comparators and multiplexers. Thus, the hybrid architecture requires only two analog-to-digital converters for error signals, regardless of the number of error microphones. Experiments were conducted on two active noise control casings, demonstrating that, with a specific computational capacity, the proposed hybrid architecture can accommodate significantly longer control filters, resulting in higher steady-state noise reduction. Moreover, additional error microphones were introduced to demonstrate the scalability. The hybrid architecture simplifies the implementation of the multi-channel feedforward active noise control system when additional error microphones are needed for more evenly distributed residual noise levels or a larger zone of quiet. • Hybrid architecture for rapid development and swift deployment of MCFFANC systems. • Analog circuit dedicated to selecting two error microphones for scalability. • Complementing PU FxLMS algorithm light to implement without degrading performance. • Proven effectiveness for high sample rate ANC applications, like casings or windows. [ABSTRACT FROM AUTHOR]

This paper presents an offline iterative scheme to efficiently obtain a fixed controller for larger-scale active noise control systems. The approach reduces the computational complexity of the scheme by applying preconditioning and prewhitening via the frequency domain, followed by conversion of the filters to the time domain using the inverse Fourier transform. Although the filters are not necessarily causal, causality is ensured by utilizing delay and truncation techniques. The use of these filters in the iterative scheme leads to improved convergence, lowering computational effort while also being memory efficient. Regularization is applied to maintain convergence while allowing for larger step sizes between iterations. The controller minimizes the reflected sound field, which is measured by the performance sensors. The signals of the performance sensors are computed with the Kirchhoff–Helmholtz integral. The results show that this allows for global control of the reflected sound field within the microphone area. A simulation is shown of a noise control setup with 12 primary sources, 12 secondary sources, 12 reference sensors, 12 error sensors and 37 performance sensors. Although the delay of the filters in the direct path is reflected in the filter coefficients, this delay can be truncated to obtain filter coefficients without any delay. Despite the truncation resulting in an average loss in performance of 1.6 dB, the control system without any delays is able to reduce the average reflected sound at the performance sensors by 13.8 dB, when the reference signal is known. • Reflections in a room are suppressed using active noise control. • Preconditioning- and prewhitening-filters are derived in the frequency domain. • The reflected sound field is computed using the Kirchhoff–Helmholtz integral. [ABSTRACT FROM AUTHOR]

• Hybrid control strategy to reduce radiated sound from a coated shell is proposed. • Vibration absorbers attenuate peaks in sound power at shell circumferential modes. • Active control suppresses sound power at spring-mass resonance of the voided coating. A hybrid passive-active control system composed of an array of inertial actuators is designed to attenuate sound radiation from a coated shell submerged in water. The coating embedded with cavities reduces the radiated sound from the shell over a broad frequency range. However, amplification in acoustic pressure occurs in narrow frequency bands associated with a spring-mass resonance of the coated shell as well as structural resonances. To address these resonances, we optimise the design of a hybrid passive-active control system composed of an array of inertial actuators tuned to different frequencies. Results show that the passive components of the inertial actuators can yield broadband reduction in the radiated sound at the shell circumferential resonances but are mostly ineffective at the spring-mass resonance. A hybrid control system in which the passive and active components are employed to target different resonances simultaneously is shown to effectively suppress the radiated sound from the coated shell. [ABSTRACT FROM AUTHOR]

In an active vibration control (AVC) system, the secondary path can generally only be identified when the system has been built. The performance of a typical adaptive feedforward control system using the filtered-x least mean square (FxLMS) algorithm is heavily dependent upon the dynamic characteristics of the secondary path. It would, therefore, be helpful for engineers to have knowledge of this behavior early in the design process. In this paper, pre-identification of the secondary path is proposed, which means its offline identification model can be obtained without installing the AVC equipment. This can save both time and money. In the pre-identification process, the secondary path is split into three parts. The first part includes the actuator and related electrical equipment. The frequency response function of this part can be identified after the hardware has been selected. The second part involves the dynamic behavior of the structure to be controlled, which is identified by impact excitation using a specially developed pneumatic impact harmer. The third part concerns the analogue/digital and digital/analogue converters whose effect on the secondary path is offset by a phase shift corresponding to half the sampling time. In this paper, the pre-identification process of the secondary path components is described, and some experiments are conducted to verify the feasibility of the approach and to demonstrate the significance of the pre-identification results. This mainly involves predicting the control performance and optimizing the parameters of the control system. Combined with the simulation of the control system, the approach proposed can improve the work efficiency of large-scale engineering projects and reduce the cost of testing. [ABSTRACT FROM AUTHOR]

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

This study employs a 1.6 m diameter rotor to validate the global active noise control method, developed from our previously work, which is based on near-field acoustic holography and sound field reproduction. The validation is conducted through experiments in an anechoic chamber. Additionally, we achieved control over nearly all acoustic modal components of multiple blade passing frequency (BPF) noises of the rotor by extending the time-domain least mean square (LMS) to the acoustic modal domain. The experimental results from near-field acoustic holography revealed that each BPF noise contained 3–4 main acoustic modal components, accounting for almost 90 % of the sound power. The global active noise control experiments demonstrated that the adaptive algorithm in the acoustic modal domain effectively mitigates the influence of rotation speed fluctuations, resulting in steady-state global noise reduction through sound field reproduction. Furthermore, for the first 3 BPF noise, our method achieved noise reduction of 9, 11, and 6 dB by controlling the first 3–4 main acoustic modal components, respectively. The concurrent reduction of various harmonic noises at all observation points validates the method's capability to globally control multiple harmonic noises. [ABSTRACT FROM AUTHOR]

In this paper, a new hybrid passive-active sound absorption method is investigated, in which porous materials and a loudspeaker are parallelly combined so that sound absorption over the full frequency band could be expected. The target impedance of the loudspeaker's diaphragm is theoretically derived under this parallel structure, and a novel active impedance control method is further proposed. In the proposed control method, dual microphones installed on the surface of the composite structure as well as in the back cavity of the loudspeaker are used to pick up the sound pressure and the velocity of the loudspeaker's diaphragm, respectively. Modal expansion analyses are performed theoretically to find the optimal microphone placement on the structure surface. An error signal is then established with an auxiliary filter corresponding to the target impedance as well as an extra delay. With the help of this extra delay, a stable and causal design result of the auxiliary filter could be obtained by solving a weighted least square problem. Finally, the impedance control of the loudspeaker's diaphragm is realized by minimizing the energy of the established error signal with the filtered-x least mean square algorithm. Both simulations and experiments are carried out based on a standing wave tube, through which it has been verified that the proposed active impedance control method can effectively enhance the low-frequency sound absorption performance of porous materials. The proposed composite structure with a thickness of 80 mm can exhibit a sound absorption coefficient greater than 0.9 for all the frequencies above 20 Hz. [ABSTRACT FROM AUTHOR]

The Volterra filter is proven to be a structurally simple method for nonlinear active noise control (NLANC). However, its wide application is deeply constrained by computational complexity. This paper proposes a tap-decomposed Volterra filter, in which a long-tap Volterra filter is approximated as multiple short-tap filters by the nearest Kronecker decomposition. Since the number of taps of decomposed filters is much less than that of the original counterpart, the proposed method greatly reduces the implementation cost of the Volterra filter. Moreover, for impulsive noise sources in NLANC, decomposed filters are updated using the maximum correntropy criterion derived from information theory. Simulation experiments with different noise source types highlight the noise control performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The frequency of an internal synthetic reference signal that is inconsistent with that of the noise signal is a serious problem in narrowband active noise control (NANC) system. This is called frequency mismatch (FM), which severely deteriorates the performance of the system. To alleviate this problem, a multichannel feedback NANC system with a coarse frequency estimator and spectrum shifter is proposed in this paper. Rough estimations of multiple tones are provided by a coarse frequency estimator based on discrete Fourier transform (DFT) coefficients and fed into the spectrum shifter. Splitting the noise signal into several individual orthogonal reference signals by the spectrum shifter rather than internal generation can avoid FM problem. Because estimated frequency errors can be tolerated in the spectrum shifter but cannot be compensated by signal generators. Compared with existing methodologies, the proposed method relaxes the demand on the precision of the frequency estimator, and reduces the undesired delay in the frequency estimation by using smaller fast Fourier transform (FFT) blocks. Moreover, a computational complexity analysis of the proposed method indicates that the proposed method is advantageous in terms of computational complexity. Extensive simulations and real-time experiments were conducted to verify the superior noise reduction performance and faster convergence speed of the proposed method under different FM situations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Over the past decade, compact spherical loudspeaker arrays have emerged as directivity controlled electroacoustic sources. The usual approach to control such a device is to provide it with a finite set of pre-programmed directivities corresponding to spherical harmonics or to the acoustic radiation modes (ARMs) of the array. However, these orthogonal bases are not suited for the acoustic control in the near field, which requires a number of spherical harmonics larger than the number of loudspeakers, and modes that take into account the reactive part of the complex sound power, not only the active part as is the case for the conventional ARMs. This paper presents a theoretical and numerical study of alternative ARM approaches applied to spherical loudspeaker arrays that consider both components of the sound power, with focus on the arrangements following the regularity of the five Platonic solids. For such arrays, it is shown that the active, reactive, and the so-called generalized ARMs form exactly the same basis, which does not depend either on frequency, source-receptor distance or on the vibration pattern and shape of the individual loudspeakers, provided that these are identical and axisymmetric. Closed-form expressions for the modal active and reactive efficiencies are derived and evaluated for the Platonic solid loudspeakers as a function of frequency and source-receptor distance. In addition, an application example in local active noise control is presented, in which a spherical loudspeaker array is used as a secondary source aiming at producing a zone of quiet in its near field. [ABSTRACT FROM AUTHOR]

Adaptive feedforward broad-band noise compensation is currently used when a correlated measurement with the disturbance (an image of the disturbance) is available. Most of the active feedforward noise control systems feature an internal "positive" acoustical feedback between the compensation system and the reference source (a correlated measurement with the disturbance) that has to be taken into account. Adaptive algorithms for active feedforward noise attenuation have been implemented such that the propagation delay between the compensatory actuator and the measurement of the residual noise (the secondary path) be much smaller than the propagation delay between the reference source (image of the incoming noise) and the measurement of the residual noise (the primary path). Nevertheless, there are potential fields of applications in which the propagation delay of the secondary path may be larger than the one of the primary path. The present paper explores the behaviour of the available adaptive feedforward compensation algorithms in this new context. The algorithms have been tested experimentally on a relevant test bench. All the algorithms except the Youla–Kučera finite impulse response (YKFIR) adaptive compensator and the standard FIR adaptive compensator using a stability based filtered adaptation (FUSBA) lead to an unstable behaviour. In terms of performance the YKFIR provides the best performance. [ABSTRACT FROM AUTHOR]

This paper proposes a method for global active noise control in vibro-acoustic cavities using acoustic sensing under the presence of both structural and acoustic disturbances. Global active noise control has earlier been carried out by minimizing the acoustic potential energy in the cavity expressed as the sum of squares of the acoustic pressures at discrete microphone locations. However, this approximation though holds well for a rectangular box cavity, is not applicable for the cavities of other shapes. This calls for development of an active noise control method based on the estimation of the acoustic potential energy applicable to the cavities of arbitrary shapes as encountered in practice. Virtual sensing of the acoustic potential energy has been reported in the literature based on measurement of vibration by the structural sensors. However, such approaches are applicable when only the structural disturbances act on the cavity, but cannot be used if the acoustic disturbances are also present in the cavity. This paper presents development of a global active noise control method based on virtual sensing of the acoustic potential energy in the cavity under the presence of structural and acoustic disturbances. Minimisation of the estimated acoustic potential energy through a feedforward control law is developed. Numerical studies on an irregular-shaped vibro-acoustic cavity and a car cavity are presented to evaluate the performance of the proposed technique. The results are also compared with the maximum possible reduction in the acoustic potential energy obtained using the optimal control inputs. The levels of reduction in the acoustic potential energy obtained with the proposed method are found to be close to the maximum possible reduction using the optimal control inputs. The proposed method is then also validated through an experimental study on a rectangular box cavity. [ABSTRACT FROM AUTHOR]

Narrowband active noise control (NANC) achieves good performance in controlling low-frequency periodic noise. Such control is necessary to obtain the frequency information of the primary periodic noise, including the number of frequency components and the individual frequency values, to generate reference signals. The performance of the NANC system mainly depends on the accuracy of frequency estimation of the primary periodic noise. In this article, a frequency estimator based on Bayesian inference that can obtain accurate frequencies as well as the number of frequency components is applied to a NANC system. Numerous computer simulations are conducted. Simulation results show that the proposed system outperforms a conventional NANC system with the adaptive notch filter (ANF) algorithm at low signal-to-noise-ratio (SNR) primary periodic noise. [ABSTRACT FROM AUTHOR]

• A nonlinear ANC system using signal decomposition and Chebyshev filter is proposed. • The non-stationary primary noise is decomposed by the improved correlated EMD. • The Chebyshev nonlinear filter is investigated to expand the decomposed IMFs. • Simulation results indicate that proposed method outperforms conventional methods. Investigations into active noise control (ANC) have been widely conducted with the aim of effective control of low-frequency noise. This paper proposes a novel nonlinear ANC system to control non-stationary noise produced by rotating machinery under nonlinear primary path. A real-time correlated empirical mode decomposition (CEMD) is first presented to decompose non-stationary noise into intrinsic mode functions (IMFs), some of which are chosen by the correlation analysis between IMFs and primary noise. Subsequently, the second-order Chebyshev nonlinear filter is applied to expand selected IMFs that are controlled individually by the filtered-x LMS. The convergence of proposed nonlinear ANC system is also investigated. Simulation results demonstrate that proposed method outperforms the filtered-x LMS, filtered-s LMS and Volterra filtered-x LMS algorithms with respect to noise reduction and convergence rate. [ABSTRACT FROM AUTHOR]

• This article addresses the adaptive inverse control of boring bar chatter in internal turning process. • The feedback Filtered-x Normalized Least Mean Square algorithm is implemented in practice. • The parameters of controller are obtained from impact tests, without a priori knowledge of cutting dynamics. • The feedback FxNLMS controller suppresses the self-excited chatter vibrations by at least 70 dBs. • The boundary of stability in internal turning process is raised by at least 10 folds. In this article, adaptive inverse control of chatter vibrations in internal turning process is addressed. The active boring bar is composed of a slender steel cutting tool, an electrodynamic shaker as controllable actuator and an IEPE accelerometer as feedback sensor. The SISO control system actuates in the direction normal to the cut surface. A novel adaptive inverse control algorithm is presented in this paper. This algorithm is mainly used in the feed-forward Active Noise Control (ANC) applications and is known as the Filtered-x Normalized Least Mean Square (FxNLMS). In order to extend the application of adaptive inverse control algorithms in the field of Active Vibration Control (AVC), the FxNLMS algorithm with feedback architecture is suggested for the specific problem of chatter suppression in internal turning operations. The performance of developed adaptive feedback controller is experimentally verified during the internal turning of Aluminum alloy 6063-T6. The value of critical limiting depth of cut is anticipated to be nearly 0.2 [mm] for the slender boring bar. However, the stable cutting process is conducted by the active boring bar in the presence of adaptive controller up to depth of cut 2 [mm]. That is, the boundary of stability is enhanced by at least 10 folds. It has been observed that the amplitude of chatter vibrations is efficiently suppressed adjacent to the fundamental resonance peak of the active boring bar. In addition, the feedback FxNLMS controller effectively attenuates the boring bar's vibration by at least 70 dBs. Moreover, the periodic chatter marks are eliminated from the surface texture of workpiece and the roughness of cut surface is remarkably improved. The obtained results suggest that the practical application of adaptive inverse control algorithms for chatter rejection can be extended to other machining processes as well. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights • The filtered-x least mean square/fourth (FXLMS/F) algorithm for active noise control. • A convex combination of the FXLMS/F algorithm (C-FXLMS/F) for active noise control. • The stability condition of the proposed algorithm is analyzed, and computational complexity is provided. • Computer simulations demonstrate good convergence speed and noise reduction performance for active noise control. Abstract The filtered-x least mean square (FXLMS) algorithm is widely used for active noise control (ANC) systems. However, due to the fixed step-size of FXLMS algorithm being used, the FXLMS algorithm results in a compromise between noise reduction performance and convergence speed. Therefore, this paper proposes the filtered-x least mean square/fourth (FXLMS/F) algorithm for ANC systems, which can be viewed as a variable step-size FXLMS (VSS-FXLMS) algorithm. In order to further improve the algorithm performance, the convex combination of the FXLMS/F (C-FXLMS/F) algorithm for ANC systems is presented. Moreover, the computational complexity of the proposed algorithms is analyzed, and a stability condition for the proposed algorithms is provided. Simulation results show that the proposed FXLMS/F and C-FXLMS/F algorithms can achieve better convergence performance as compared to the FXLMS and FXLMF algorithms under various noise input conditions, and the C-FXLMS/F algorithm outperforms the FXLMS/F algorithm. [ABSTRACT FROM AUTHOR]

Active noise control (ANC) is broadly used to cancel the unwanted disturbance in different fields because of its excellent performance in abating low-frequency noise. In practice, however, the limited driving capability of actuators restrict the maximum output power of ANC systems. Once the driving signal of the ANC system exceeds these limitations, the inherent nonlinearity of the actuators will deteriorate the noise reduction and may result in the divergence of the adaptive algorithm. Hence, the two-gradient direction filtered-x least mean square (2GD-FXLMS) algorithm based on the optimal Kuhn-Tucker solution with the output constraint is proposed in this paper. This algorithm has the advantage of minimizing system overdriving, maintaining a specified power budget, and enhancing system stability. Compared to existing output-constrained adaptive algorithms, this proposed algorithm has the same computational complexity as the conventional FXLMS algorithm, while maintaining a stricter output constraint that minimizes the saturation distortion. [ABSTRACT FROM AUTHOR]

Narrowband active noise control (ANC) conventionally uses the waveform synthesis method in which a synchronization signal is essential. In addition to this, to update the parameters of the controller, prior estimation of the secondary path is required. Any mismatch in the frequency of the synchronization signal and the secondary path estimate compared to actual leads to a degraded performance in noise control. In this paper, a new type of narrowband ANC algorithm is proposed which requires neither the synchronization signal nor the secondary path estimate for controller tuning. Therefore, the algorithm is free from the effect of the change in the source frequency and the secondary path transfer function. The proposed adaptive algorithm is based on a non-gradient type optimization algorithm named as particle swarm optimization (PSO) algorithm. Through exhaustive computer based simulation, it has been demonstrated that the algorithm is not only capable to control a monotone noise but also efficiently control a multi-tone noise. The algorithm, due to its adaptive nature, is also capable of tracking the change in amplitude and frequency of the noise as well as, the change in the characteristics of primary and secondary paths. [ABSTRACT FROM AUTHOR]

In this case study, the upper limit frequency for global noise control in car cabins is investigated with numerical simulations, and experimental results of global control are presented for the first time. To simulate the complicated noise field in a running car, a 1.90 m × 1.50 m × 1.10 m rectangular enclosure, which is approximately the size of a typical car is used with point monopoles evenly distributed near the boundaries generating the primary sound field. Besides verifying the fact that the upper limit frequency of effective control increases with the number of secondary sources, a formula is proposed in this paper to estimate the number of secondary sources required to achieve global control up to a certain frequency. The simulations are carried out in the time domain based on the transfer functions measured between the secondary loudspeakers and 40 evaluation points evenly distributed in a typical car, which show that the upper limit frequency of 3 dB global control is about 200 Hz while using 4 secondary sources and it can be improved to 315 Hz with 7 secondary sources. An active noise control system is realized with 16 accelerometers picking up vibration reference signals and 4 or 7 loudspeakers as secondary sources, and the real time test results in the car running at 50 km/h on a typical asphalt road demonstrated the validity of the findings in numerical simulations. [ABSTRACT FROM AUTHOR]

The use of Machine Learning (ML) has rapidly spread across several fields of applied sciences, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). An advantage of ML algorithms compared to traditional techniques is that physical phenomena can be modeled using only sampled data from either measurements or simulations. This is particularly important in SD&V when the model of the studied phenomenon is either unknown or computationally expensive to simulate. This paper presents a survey on the application of ML algorithms in three classical problems of SD&V: structural health monitoring, active control of noise and vibration, and vibroacoustic product design. In structural health monitoring, ML is employed to extract damage-sensitive features from sampled data and to detect, localize, assess, and forecast failures in the structure. In active control of noise and vibration, ML techniques are used in the identification of state-space models of the controlled system, dimensionality reduction of existing models, and design of controllers. In vibroacoustic product design, ML algorithms can create surrogates that are faster to evaluate than physics-based models. The methodologies considered in this work are analyzed in terms of their strength and limitations for each of the three considered SD&V problems. Moreover, the paper considers the role of digital twins and physics-guided ML to overcome current challenges and lay the foundations for future research in the field. • Review of machine learning applied in vibroacoustic and structural dynamics. • Basic principles of the machine learning methods commonly used are presented. • Control, structural health monitoring and design are the most relevant applications. • The current implementation scenario and challenges are reviewed for each application. • This article sheds light on trends and research opportunities merging both fields. [ABSTRACT FROM AUTHOR]

The paper explores in detail the use of dynamic adaptation gain/learning rate (DAG) for improving the performance of adaptive feedforward attenuation schemes. The DAG is an ARMA (poles-zeros) filter embedded in gradient type adaptation/learning algorithms and generalizes the various improved gradient algorithms available in the literature. After introducing the DAG algorithm in the context of adaptive feedforward attenuation schemes and providing relationships with other algorithms, its design is developed. Strictly Positive Real (SPR) conditions play an important role in the design of the DAG. Then the stability issues for adaptive/learning systems using a DAG are discussed. The potential of the DAG is then illustrated by experimental results obtained on a relevant adaptive active noise control system. • It proposes a new adaptation/learning algorithm. • The methodology of using this new algorithm is presented. • Applicability to active noise control systems is illustrated. • Accelerated adaptation/learning transients are obtained. [ABSTRACT FROM AUTHOR]

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) algorithm 1 1 The 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. • McALMS is rederived without assumptions of input independence or Gaussian noise. • McALMS attains the same optimal solution as the McFxLMS algorithm. • Step size estimated with GSS approach the actual bound in practice. • McALMS achieves global noise reduction with lower complexity than McFxLMS. [ABSTRACT FROM AUTHOR]

The filtered-s maximum correntropy criterion (FsMCC) algorithm was developed for nonlinear active noise control (NANC) in impulsive noise environments with the use of functional linked artificial neural networks (FLANNs) structure. However, the kernel-width of the FsMCC is constant, which is dull and cannot self-regulate to achieve both the fast convergence and low steady-state error performance. For better convergence ability, in this paper, a fractional-order gradient ascent filtered-s maximum correntropy criterion algorithm (FGA-FsMCC) is presented for multi-channel NANC system. The fractional lower-order moments supersede the non-existent second-order moments. To come up with a more reliable algorithm, by merging two kernels with complementary width, the fractional-order gradient ascent filtered-s mixture correntropy criterion (FGA-FsMMCC) was presented. The computational complexity and convergence conditions are analyzed. To verify the robustness of proposed algorithms, simulations are carried out to discuss parameters and control effects of algorithms for alpha-stable noise in different nonlinear environments. • Design multi-channel nonlinear active noise control system with functional linked artificial neural networks. • Proposing a fractional-order gradient ascent filtered-s maximum correntropy criterion algorithm. • Defining the maximum mixture correntropy criterion. • Deriving a fractional-order gradient ascent filtered-s maximum mixture correntropy criterion algorithm. • Computational complexity analysis of proposed algorithms. [ABSTRACT FROM AUTHOR]

For active control of road noise inside a vehicle, many reference signals are required in adaptive control systems, resulting in slow convergence speeds and heavy computational loads. In this paper, a means of reducing the computational complexity of adaptive control algorithms is investigated based on the fast-converging least-mean-squares Newton (LMS-Newton) algorithm. By analyzing the autocorrelation matrix of reference signals filtered by secondary paths, it is found that the dimensions of the autocorrelation matrix of filtered reference signals do not necessarily have to equal the dimensions of the adaptive control filters, and instead the dimensions can be determined from the correlation between reference signals filtered by secondary paths. Then a computationally efficient segment-update block algorithm is proposed that splits adaptive control filters into several segments and the coefficients of each segment are updated independently. Theoretical analysis shows that the proposed segment-update block algorithm has similar convergence speed and noise-reduction performance as the traditional LMS-Newton algorithm when a segment of appropriate length is chosen, but the computational complexity is reduced dramatically because the inverse autocorrelation matrix is replaced by a smaller matrix. The performance of the proposed algorithm is verified through simulation experiments with different recorded road noises and secondary paths, and the results are compared with those of some typical algorithms. The simulation results showed that the proposed algorithm achieved a similar noise-reduction performance with an approximately 8.9-times reduction of computational complexity compared with the traditional LMS-Newton algorithm. [ABSTRACT FROM AUTHOR]

Abstract As one of the most commonly used nonlinear active noise control (NANC) algorithms, the filtered-s least mean square (FsLMS) algorithm outperforms the conventional filtered-x least mean square (FxLMS) algorithm when the primary path has a quadratic nonlinearity. However, it still suffers from performance degradation under strong interferences. In this paper, two new algorithms, named filtered-s q -least mean p -norm (FsqLMP) and filtered-s q -least mean square (FsqLMS), based on the concept of Jackson's derivative, are proposed. By using new Jackson's derivative method, the proposed algorithms are less sensitive to the interferences in NANC system. Additionally, it is shown that the family of q -least mean square algorithms are special cases of the proposed FsqLMP algorithm. To further improve performance of the FsqLMS algorithm and solve the parameter selection problem, a time varying q scheme is developed. Simulation studies indicate that the proposed algorithms provide superior performance in various noise environments as compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes an algorithm called as 'Modal filtered-x LMS algorithm' for global active noise control in vibro-acoustic cavities. The proposed algorithm is a formulation of the conventional filtered-x least mean square algorithm in modal domain to reduce acoustic potential energy in a cavity. The formulation leads to a concept of what has been called as 'modal secondary path' in the paper. The active control method makes use of the reference signal filtered using modal secondary paths instead of the reference signal filtered using physical secondary paths as done in the conventional FxLMS algorithm. The method requires knowledge of acoustic modal pressures which in the present paper are estimated using acoustic pressure measurements at a discrete number of points in the cavity. The proposed method allows a modal based control of acoustic potential energy in the cavity. The proposed method also has an advantage that it reduces the computational burden associated with filtering of the reference signal with secondary paths for global control. A numerical study to actively control noise due to structural and acoustic disturbances acting on a car-like cavity is presented. Results of modal based control are compared with maximum reduction that is possible for given disturbances and control sources. It is found that the noise reduction obtained using the modal based approach is close to the maximum possible but at a lower computational cost. [ABSTRACT FROM AUTHOR]

A variable step-size median least-mean-square (VS-MLMS) algorithm, for active noise control (ANC) of rail vehicle interior noises is presented in this paper. Firstly, the interior noises of an urban rail vehicle under normal operating conditions are measured and analysed. Results show that the interior noise includes obvious impact noise components, and the sound energy of this noise is mainly concentrated in a low-frequency range below 1000 Hz. Utilising the measured interior noises, the adaptive filter order, median window length, and the optimal step size of the MLMS algorithm are determined. According to the step-size scope, the VS-MLMS algorithm is developed using a Sine step-size adjusting function. The MLMS, VS-MLMS and other algorithms are applied to the measured interior noise signals. Results suggest that the proposed VS-MLMS, which can balance the convergence speed and the steady-state error well, is more suitable for active control of a low-frequency noise with impact noise components. The VS-MLMS algorithm can be directly used in an ANC system of the sample urban rail vehicle, which is helpful to improve the ride comfort of the passengers. [ABSTRACT FROM AUTHOR]

The standard adaptive filtering algorithm with a single error norm exhibits slow convergence rate and poor noise reduction performance under specific environments. To overcome this drawback, a filtered-x generalized mixed norm (FXGMN) algorithm for active noise control (ANC) system is proposed. The FXGMN algorithm is developed by using a convex mixture of l p and l q norms as the cost function that it can be viewed as a generalized version of the most existing adaptive filtering algorithms, and it will reduce to a specific algorithm by choosing certain parameters. Especially, it can be used to solve the ANC under Gaussian and non-Gaussian noise environments (including impulsive noise with symmetric α -stable ( S α S ) distribution). To further enhance the algorithm performance, namely convergence speed and noise reduction performance, a convex combination of the FXGMN algorithm (C-FXGMN) is presented. Moreover, the computational complexity of the proposed algorithms is analyzed, and a stability condition for the proposed algorithms is provided. Simulation results show that the proposed FXGMN and C-FXGMN algorithms can achieve better convergence speed and higher noise reduction as compared to other existing algorithms under various noise input conditions, and the C-FXGMN algorithm outperforms the FXGMN. [ABSTRACT FROM AUTHOR]

To tune the noise amplification in the feedback system caused by the waterbed effect effectively, an adaptive algorithm is proposed in this paper by replacing the scalar leaky factor of the leaky FxLMS algorithm with a real symmetric Toeplitz matrix. The elements in the matrix are calculated explicitly according to the noise amplification constraints, which are defined based on a simple but efficient method. Simulations in an ANC headphone application demonstrate that the proposed algorithm can adjust the frequency band of noise amplification more effectively than the FxLMS algorithm and the leaky FxLMS algorithm. [ABSTRACT FROM AUTHOR]

The sound absorption performance of an electroacoustic absorber (EA) is primarily influenced by the dynamic characteristics of the loudspeaker that acts as the actuator of the EA system. Therefore, the sound absorption performance of the EA is maximum at the resonance frequency of the loudspeaker and tends to degrade in the low-frequency and high-frequency bands based on this resonance frequency. In this study, to adjust the sound absorption performance of the EA system in the low-frequency band of approximately 20–80 Hz, an EA system using a vented enclosure that has previously been used to enhance the radiating sound pressure of a loudspeaker in the low-frequency band, is proposed. To verify the usefulness of the proposed system, two acoustic environments are considered. In the first acoustic environment, the vent of the vented enclosure is connected to an external sound field that is distinct from the sound field coupled to the EA. In this case, the acoustic effect of the vented enclosure on the performance of the EA is analyzed through an analytical approach using dynamic equations and an impedance-based equivalent circuit. Then, it is verified through numerical and experimental approaches. Next, in the second acoustic environment, the vent is connected to the same external sound field as the EA. In this case, the effect of the vented enclosure on the EA is investigated through an analytical approach and finally verified through a numerical approach. As a result, it is confirmed that the characteristics of the sound absorption performances of the proposed EA system using the vented enclosure in the two acoustic environments considered in this study are different from each other in the low-frequency band of approximately 20–80 Hz. Furthermore, several case studies on the change tendency of the performance of the EA using the vented enclosure according to the critical design factors or vent number for the vented enclosure are also investigated. In the future, even if the proposed EA system using a vented enclosure is extended to a large number of arrays required for 3D sound field control, it is expected to be an attractive solution that can contribute to an improvement in low-frequency noise reduction without causing economic and system complexity problems. [ABSTRACT FROM AUTHOR]

Active noise control (ANC) methods have been extensively used for reducing the noise level over the past decade. In particular, in the range of low frequencies they can effectively reduce the noise level higher than passive control methods. However, except in some special cases, in ANC only local zones of quiet are obtained. In this paper one of such exceptions is investigated—an active noise-controlling casing. More specifically, a noise source is placed inside the casing. Casing vibrations are actively controlled to achieve global noise reduction in free-field conditions. The global noise reduction is obtained using the Switched Error FXLMS algorithm and using error signals from error microphones located at appropriate points around the casing. It is shown that selected microphone array configurations can provide global noise reduction in free-field conditions. Performance of this approach is experimentally verified in a laboratory room with an exemplary acoustic treatment. [ABSTRACT FROM AUTHOR]

• A partial subband update strategy is proposed to reduce the computational complexity of the adaptive algorithms. • A weighted reference signal normalized filtered-x least mean square algorithm is designed for subband filters to improve the convergence rate and reduce the round-off error. • A purely time-delayed secondary path is designed to eliminate the negative impact of the magnitude response of the secondary path. • A parameter observer is proposed and a parameter adjusting strategy is designed based on this observer. Road noise is one of the dominant noise sources in pure electric vehicles. Active control for road noise is an attractive solution since this technique is suitable for controlling low-frequency noise. Feed-forward active road noise control (ARNC) systems utilize lots of reference signals and corresponding adaptive filters, and the control performance usually improves as the number of reference signals increases. To reduce the computational cost of ARNC systems so that more reference signals can be processed, we introduce the delayless subband structure into ARNC system and a delayless partial update subband with weighted reference signal normalized filtered-x least mean square (DPUS-WRS-NFxLMS) algorithm is proposed in this paper. Based on the traditional delayless subband algorithm, a partial subband update strategy is proposed to reduce the computational cost. Furthermore, the weighted reference signal is introduced for each subband filter. In this way, a modified vector is designed to assign different weights to different reference signal samples so that the convergence performance can be improved and the round-off error can be reduced. In addition, an adjustment strategy for the step size and reference signal weights is designed to make the algorithm have a better balance between the convergence rate and steady-state behavior. Finally, a purely time-delayed secondary path is constructed as the estimated secondary path to avoid the negative impact of the magnitude-frequency characteristics of the actual secondary path. The simulation results prove that the proposed algorithm achieves better control results than the traditional algorithms with lower computational complexity. [ABSTRACT FROM AUTHOR]

In the field of active noise and vibration control (ANVC), a considerable part of unwelcome noise and vibration is resulted from rotational machines, making the spectrum of response signal multiple-frequency. Narrowband filtered-x least mean square (NFXLMS) is a very popular algorithm to suppress such noise and vibration. It has good performance since a priori-knowledge of fundamental frequency of the noise source (called reference frequency) is adopted. However, if the priori-knowledge is inaccurate, the control performance will be dramatically degraded. This phenomenon is called reference frequency mismatch (RFM). In this paper, a novel narrowband ANVC algorithm with orthogonal pair-wise reference frequency regulator is proposed to compensate for the RFM problem. Firstly, the RFM phenomenon in traditional NFXLMS is closely investigated both analytically and numerically. The results show that RFM changes the parameter estimation problem of the adaptive controller into a parameter tracking problem. Then, adaptive sinusoidal oscillators with output rectification are introduced as the reference frequency regulator to compensate for the RFM problem. The simulation results show that the proposed algorithm can dramatically suppress the multiple-frequency noise and vibration with an improved convergence rate whether or not there is RFM. Finally, case studies using experimental data are conducted under the conditions of none, small and large RFM. The shaft radial run-out signal of a rotor test-platform is applied to simulate the primary noise, and an IIR model identified from a real steel structure is applied to simulate the secondary path. The results further verify the robustness and effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]