Robust active control of broadband noise in finite ducts

This study focuses on robust active control of broadband noise in finite ducts. Our analytical and experimental studies suggest the existence of several technical flaws in the path models of conventional active noise control (ANC) systems. These are sensitivity of the path model with respect to boundary conditions, and nonminimum phase (NMP) secondary and reference paths. For finite ducts with small cross sections, the traveling wave model (TWM) may be adopted to find an effective solution to these problems and lead to a robust ANC system. Since many practical "noisy" ducts are finite with small cross sections, the proposed ANC has many practical applications. Its robustness and ability to suppress broadband noise will be explained theoretically and verified experimentally.     

Active control of the volume acquisition noise in functional magnetic resonance imaging: method and psychoacoustical evaluation.

Functional magnetic resonance imaging (fMRI) provides a noninvasive tool for observing correlates of neural activity in the brain while a subject listens to sound. However, intense acoustic noise is generated in the process of capturing MR images. This noise stimulates the auditory nervous system, limiting the dynamic range available for displaying stimulus-driven activity. The noise is potentially damaging to hearing and is distracting for the subject. In an active noise control (ANC) system, a reference sample of a noise is processed to form a sound which adds destructively with the noise at the listener's ear. We describe an implementation of ANC in the electromagnetically hostile and physically compact MRI scanning environment. First, a prototype system was evaluated psychoacoustically in the laboratory, using the electrical drive to a noise-generating loudspeaker as the reference. This system produced 10-20 dB of subjective noise-reduction between 250 Hz and 1 kHz, and smaller amounts at higher frequencies. The system was modified to operate in a real MR scanner where the reference was obtained by recording the acoustic scanner noise. Objective reduction by 30-40 dB of the most intense component in scanner noises was realized between 500 Hz and 3500 Hz, and subjective reduction of 12 dB and 5 dB in tests at frequencies of 600 Hz and at 1.9 kHz, respectively. Although the benefit of ANC is limited by transmission paths to the cochlea other than air-conduction routes from the auditory meatus, ANC achieves worthwhile attenuation even in the frequency range of maximum bone conduction (1.5-2 kHz). ANC should, therefore, be generally useful during auditory fMRI.     

A nonlinear active noise control algorithm for virtual microphones controlling chaotic noise

In active noise control (ANC) systems, virtual microphones provide a means of projecting the zone of quiet away from the physical microphone to a remote location. To date, linear ANC algorithms, such as the filtered-x least mean square (FXLMS) algorithm, have been used with virtual sensing techniques. In this paper, a nonlinear ANC algorithm is developed for a virtual microphone by integrating the remote microphone technique with the filtered-s least mean square (FSLMS) algorithm. The proposed algorithm is evaluated experimentally in the cancellation of chaotic noise in a one-dimensional duct. The secondary paths evaluated experimentally exhibit non-minimum phase response and hence poor performance is obtained with the conventional FXLMS algorithm compared to the proposed FSLMS based algorithm. This is because the latter is capable of predicting the chaotic signal found in many physical processes responsible for noise. In addition, the proposed algorithm is shown to outperform the FXLMS based remote microphone technique under the causality constraint (when the propagation delay of the secondary path is greater than the primary path). A number of experimental results are presented in this paper to compare the performance of the FSLMS algorithm based virtual ANC algorithm with the FXLMS based virtual ANC algorithm.     

Adaptive Laguerre filters for active noise control

Feedforward controllers are used in many active noise control (ANC) systems to generate destructive interference in noise fields. An ideal feedforward ANC controller should have an infinite impulse response (IIR) transfer function, but most available feedforward ANC controllers have finite impulse responses (FIR) instead. The main reason is related to the adaptation algorithms of ANC systems. In general, adaptive FIR filters converge faster with guaranteed stability. In this study, the adaptive Laguerre filter is proposed and tested in an ANC application with positive experimental effects. The new ANC controller is an IIR filter, but its adaptation is similar to that of a FIR filter with fast convergence and guaranteed stability. Detailed explanations and analysis are presented in the main text.     

A hybrid active noise controller for finite ducts

A hybrid active noise controller (ANC) is proposed to solve some existing problems, which are related to the non-minimum phase (NMP) path models between uncollocated sensors and actuators in many ANC systems. For hybrid ANC schemes, the NMP path causes design difficulties to both feedforward and feedback control. These problems can be solved effectively by adding an extra actuator in the ANC system. A new design procedure is presented to take the greatest advantage of the extra actuator. Theoretical analysis and experimental results are presented to show the improved performance of the proposed ANC.     

A relaxed condition for "perfect" cancellation of broadband noise in 3D enclosures

This paper presents a relaxed condition for "perfect" cancellation of broadband noise in 3D enclosures. On the basis of a truncated modal model, it can be shown that the primary and secondary paths belong to a same subspace if a certain condition is satisfied. There exists a finite impulse response (FIR) filter transfer function vector for perfect cancellation of the primary paths. The analytical result is verified numerically with an active noise control (ANC) system in a 3D rectangular enclosure. The proposed ANC scheme is shown to fit well into the framework of an existing multichannel least-mean squares (LMS) algorithm for adaptive implementation.     

Adaptive variable step-size neural controller for nonlinear feedback active noise control systems

Adaptive filter techniques and the filtered-x least mean square (FxLMS) algorithm have been used in Active Noise Control (ANC) systems. However, their effectiveness may degrade due to the nonlinearities and modeling errors in the system. In this paper, a new feedback ANC system with an adaptive neural controller and variable step-size learning parameters (VSSP) is proposed to improve the performance. A nonlinear adaptive controller with the FxLMS algorithm is first designed to replace the traditional adaptive FIR filter; then, a variable step-size learning method is developed for online updating the controller parameters. The proposed control is implemented without any offline learning phase, while faster convergence and better noise elimination can be achieved. The main contribution is that we show how to analyze the stability of the proposed closed-loop ANC systems, and prove the convergence of the presented adaptations. Moreover, the computational complexities of different methods are compared. Comparative simulation results demonstrate the validity of the proposed methods for attenuating different noise sources transferred via nonlinear paths, and show the improved performance over classical methods.     

Analogue active noise control

The aim of this article is to present in details all the stages of an analogue active noise control system. As the exemplary plant, an active personal hearing protection device is considered. In the introductory section the plant is presented and the state of the art is reported. In subsequent section, first a way of identification of a continuous-time model of the plant is described and performance limitations are analysed. Then, the procedure for designing optimal controller is proposed. It starts from analysing properties of the plant and choosing a suitable controller structure. Next, coefficients of the controller are found following the design premises. These coefficients serve as initial parameters in the optimisation process. For this process the performance index and appropriate constraints are built. Next, details on implementation of the controller using a chip with switched capacitors are given. Attenuation results of various noises are presented. Finally, cost of the hardware is evaluated.The methodology presented in this article can be considered as a detailed procedure for designing and practically realising cheap and easily tuneable controllers providing good noise attenuation and satisfying several constraints related to frequency band, stability margin, etc.     

Implementation of active noise control in a multi-modal spray dryer exhaust stack

Tonal noise emitted from large-diameter spray dryer exhaust stacks used in the dairy industry can give rise to complaints from nearby communities. In many cases, the tone at the fan blade passing frequency is characterized by a frequency above the first mode cut on frequency of the exhaust stack and both its amplitude and the frequency are time varying. The variation in amplitude is a result of turbulence and temperature variations in the duct which cause angular variations in the nodal plane of modes with diametrical nodes. This in turn results in large fluctuations in sound pressure with time at any specified location in the duct, thus presenting a significant challenge for an ANC system with fixed control source and error sensor locations. In many food processing industries, the use of sound absorptive materials in silencers is not acceptable and, particularly when the fan speed is variable, it is difficult to achieve an acceptable passive solution at a reasonable cost. Here, the design and implementation of an active noise control system for tonal noise propagating above the cut-on frequency of the first higher order mode in large size cylindrical industrial exhaust stack is discussed, where the frequency and amplitude vary significantly and relatively rapidly with time. Physical system design principles and control algorithm optimization for a practical active noise control system are presented. Finally, real time control results which were achieved by a prototype installation on a large-diameter, in-service exhaust stack are given. Significant noise reductions were achieved in the community.     

A robust filtered-s LMS algorithm for nonlinear active noise control

The performance of a nonlinear active noise control (ANC) system based on the recently developed filtered-s least mean square (FsLMS) algorithm deteriorates when strong disturbances in the ANC system are acquired by the microphones. To surmount this shortcoming, a novel robust FsLMS (RFsLMS) algorithm is proposed for a functional link artificial neural network (FLANN) based ANC system. The new ANC system is least sensitive to such disturbances and does not call for any prior information on the noise characteristics. The results obtained from simulation study establish the effectiveness of this new ANC scheme.     

RETRACTED: A Hybrid Nonlinear Active Noise Control Method Using Chebyshev Nonlinear Filter

Investigations into active noise control (ANC) technique have been conducted with the aim of effective control of the low-frequency noise. In practice, however, the performance of currently available ANC systems degrades due to the effects of nonlinearity in the primary and secondary paths, primary noise and louder speaker. This paper proposes a hybrid control structure of nonlinear ANC system to control the non-stationary noise produced by the rotating machinery on the nonlinear primary path. A fast version of ensemble empirical mode decomposition is used to decompose the non-stationary primary noise into intrinsic mode functions, which are expanded using the second-order Chebyshev nonlinear filter and then individually controlled. The convergence of the nonlinear ANC system is also discussed. Simulation results demonstrate that proposed method outperforms the FSLMS and VFXLMS algorithms with respect to noise reduction and convergence rate.     

Convex combination of nonlinear adaptive filters for active noise control

This paper proposes a nonlinear active noise control (ANC) system based on convex combination of a functional link artificial neural network (FLANN) and a Volterra filter. Simulation study reveals enhanced noise cancelation performance of the proposed ANC system over the ones based on its component filters.     

IMPLEMENTATION OF A BROADBAND DUCT ANC SYSTEM USING ADAPTIVE SPATIALLY FEEDFORWARD STRUCTURE

An adaptive spatially feedforward algorithm is proposed for broadband attenuation of noise in ducts. Acoustic feedback generally exists in this active noise control structure. Munjal and Eriksson (1988 Journal of Acoustical Society of America84, 1086-1093) derived an ideal controller for the spatially feedforward structure. The ideal controller can be partitioned into two parts. The first part represents a repetitive controller that can be implemented by an infinite impulse response (IIR) filter, whereas the second part represents the dynamics of transducer that can be implemented by a finite impulse response (FIR) filter. In the paper, the IIR filter is merged with the original plant. The FIR filter is adaptively updated by the least-mean-square (LMS) algorithm to accommodate perturbations and uncertainties in the system. The proposed algorithm is implemented via a floating point digital signal processor and compared with other commonly used algorithms such as the Filtered-X LMS algorithm, the feedback neutralization algorithm, and the Filtered-U LMS algorithm. Experimental results show that the system has attained 15·7 dB maximal attenuation in the frequency band 200-600 Hz.     

Simulations and experiments for hybrid noise control systems

The purpose of this study is to explore the effects of sound elimination in a cylindrical duct by combining a reactive muffler and active noise control (ANC) system. Besides the exploration via experiment of the combined noise control system, a Grey prediction based on Grey theory is also applied to ANC for this hybrid system.In the experiment for this system, a combined adaptive algorithm is adopted. The results of sound elimination are compared between cases with ANC systems installed before the muffler and after the muffler. The results indicate that the sequence of arrangement of muffler can influence the results of active noise control. According to the results of experiment and simulation, the effect of noise reduction in ANC system is influenced extremely by reference signal received. The transmission loss and insertion loss in this system are also discussed in details. Besides, the experimental results indicate that the hybrid system has the advantages over a traditional muffler when the muffler is not designed for the frequency of the noise. Furthermore, the mathematic simulation for acoustic field in a cylindrical duct with a muffler is performed in order to verify the experiment results. Finally, Grey theory is applied to estimate the expected signals in order to perform a computer simulation of Grey prediction to explore effects of the ANC system. The results indicate that application of Grey theory gives a good control for the hybrid system.     

Sound quality of low-frequency and car engine noises after active noise control

The ability of active noise control (ANC) systems to achieve a more pleasant sound has been evaluated by means of sound quality analysis of a real multi-channel active noise controller. Recordings of real car engine noises had been carried out using a HeadacousticsTM binaural head simulator seated in a typical car seat, and these signals together with synthesized noise have been actively controlled in an enclosed room.The sound quality study has focused on the estimation of noise quality changes through the evaluation of the sense of comfort. Two methods have been developed: firstly, a predictive method based on psychoacoustic parameters (loudness, roughness, tonality and sharpness); and secondly, a subjective method using a jury test. Both results have been related to the spectral characteristics of the sounds before and after active control.It can be concluded from both analyses that ANC positively affects acoustic comfort. The engine noise mathematical comfort predictor is based on loudness and roughness (two psychoacoustic parameters directly influenced by ANC), and has satisfactorily predicted the improvements in the pleasantness of the sounds. As far as the subjective evaluation method is concerned, the jury test has showed that acoustic comfort is, in most cases, directly related to the sense of quietness. However, ANC has also been assessed negatively by the jury in the cases that it was unable to reduce the loudness, perhaps because of the low amplitudes of the original sounds.Finally, from what has been shown, it can be said that the subjective improvements strongly depends on the attenuation level achieved by the ANC system operation, as well as the spectral characteristics of the sounds before and after control.     

Experimental results for multichannel feedforward ANC with noninvasive system identification

This paper presents experimental validation of a class of algorithms designed to enable active noise control (ANC) to function in environments when transfer functions change significantly over time. The experimental results presented are for broadband, local quieting in a diffuse field using a multichannel ANC system. The reverberant enclosure is an ordinary room, measuring approximately 1.4 x 2.4 x 2.4 m3 and containing a seated occupant, with six microphones defining the quiet zone near the occupant's ears. The control system uses a single reference signal and two error channels to drive four secondary sources. Using an ideal reference sensor, reduction in sound pressure level is obtained at the quiet-zone microphones averaged over the frequency range 50 to 1000 Hz with an occupant seated in the room. Two main results are presented: first for an adaptive cancelling algorithm that uses static system models, and second for the same algorithm joined with a noninvasive real-time system identification algorithm. In the first case better than 23 dB of performance is obtained if the occupant remains still through calibration and testing. In the second case, approximately 18 dB is obtained at the error microphones regardless of the motion of the occupant.     

Improving performance of FxRLS algorithm for active noise control of impulsive noise

Active noise control (ANC) systems employing adaptive filters suffer from stability issues in the presence of impulsive noise. New impulsive noise control algorithms based on filtered-x recursive least square (FxRLS) algorithm are presented. The FxRLS algorithm gives better convergence than the filtered-x least mean square (FxLMS) algorithm and its variants but lacks robustness in the presence of high impulsive noise. In order to improve the robustness of FxRLS algorithm for ANC of impulsive noise, two modifications are suggested. First proposed modification clips the reference and error signals while, the second modification incorporates energy of the error signal in the gain of FxRLS (MGFxRLS) algorithm. The results demonstrate improved stability and robustness of proposed modifications in the FxRLS algorithm. However, another limitation associated with the FxRLS algorithm is its computationally complex nature. In order to reduce the computational load, a hybrid algorithm based on proposed MGFxRLS and normalized step size FxLMS (NSS-FXLMS) is also developed in this paper. The proposed hybrid algorithm combines the stability of NSS-FxLMS algorithm with the fast convergence speed of the proposed MGFxRLS algorithm. The results of the proposed hybrid algorithm prove that its convergence speed is faster than that of NSS-FxLMS algorithm with computational complexity lesser than that of FxRLS algorithm.     

Comparison of convergence characteristics of adaptive IIR and FIR filters for active noise control in a duct

There are perceived drawbacks to using adaptive IIR filters, as opposed to adaptive FIR filters, for active noise control (ANC). These include stability issues, the possible convergence of estimated parameters to biased and/or local minimum solutions and relatively slow rate of convergence. Stability issues can generally be resolved easily using well-established methods. In this Technical Note convergence rates are compared with particular reference to the active control of noise in a duct, for which the dynamics of the cancellation path are important. The characteristics of this application of ANC set it apart from usual signal processing applications of adaptive IIR filters and this has implications for the convergence properties. Various control approaches are considered: IIR least mean squares (IIR-LMS), IIR recursive least squares (IIR-RLS) with FASPIS (Fast Algorithm Secondary Path Integration Scheme) and FIR-LMS. Numerical examples are presented. It is seen that the cancellation path dynamics generally have the effect of changing the performance surface of the estimated IIR filter from bimodal to unimodal, which has consequences for improving the convergence rate of adaptive IIR filters. It is also seen that IIR-RLS has a comparable rate of convergence to FIR-LMS, with the steady-state performance being as good or better.     

Time–frequency-domain filtered-x LMS algorithm for active noise control

This paper presents a time–frequency-domain filtered-x LMS (FXLMS) algorithm for active noise control (ANC) based on the short-time Fourier transform (STFT). We show that proposed algorithm has much reduced computational complexity and better convergence performance, as compared with the time-domain FXLMS algorithm. Additionally, computer simulations show that a time–frequency-domain FXLMS algorithm for ANC is effective in canceling non-stationary noise while a frequency-domain FXLMS algorithm remains inadequate at this task.     

A novel feedback active noise control for broadband chaotic noise and random noise

The feedback active noise control (ANC) can be seen as a predictor, the conventional method based on filtered-x least mean square (FXLMS) algorithm can only be useful for linear and tonal noise, but for nonlinear and broadband noise, it is useless. The feedback ANC using functional link artificial neural networks (FLANN) based on filtered-s least mean square (FSLMS) algorithm can reduce some nonlinear noise such as chaotic noise, but the noise cancellation performance is not very well, at the same time, it is not useful to random noise. To solve the problem above, a new feedback ANC using wavelet packet FXLMS (WPFXLMS) algorithm is proposed in this paper. By decomposing the broadband noise into several band-limited parts which are predictable and each part is controlled independently, the proposed algorithm can not only suppress the chaotic noise, but also mitigate the random noise. Compared with FXLMS and FSLMS algorithms, proposed WPFXLMS algorithm also holds the best performance on noise cancellation. Numerous simulations are conducted to demonstrate the effectiveness of the proposed WPFXLMS algorithm.