Active Noise Control Algorithm Research Articles

A Simplified Frequency-Domain Feedback Active Noise Control Algorithm

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

A semi-adaptive feedforward hybrid active noise control algorithm for multichannel systems.

Broadband active noise control systems incorporating fixed controllers exhibit limited ability to reduce sinusoids. This study presents a semi-adaptive feedforward hybrid active noise control (HANC) system to address this issue. The proposed system pairs fixed high-order optimal controllers for broadband noise with adaptive low-order FXLMS-based controllers for narrowband noise. Notably, parallel broadband and narrowband controllers work independently. The proposed semi-adaptive feedforward HANC system demonstrates low computational complexity which makes it suitable for multichannel systems. Simulations and experiments validate the effectiveness of the proposed system in controlling mixed noise.

Convex combination of online secondary path modeling and improved cuckoo search for active noise control

This paper proposes an active noise control (ANC) algorithm based on convex combination of the online secondary path modeling (OSPM) and improved cuckoo search (ICS) algorithms. The ICS algorithm does not request the secondary path model to optimize the control filter. Therefore, the ICS algorithm is theoretically not affected by a varying secondary path, but its convergence is very slow. In contrast, the OSPM algorithm achieves faster convergence but is vulnerable to a sudden change in the secondary path. The proposed ANC algorithm combines the merits of both the OSPM and ICS algorithms by convex combination. Simulation results show that the proposed ANC algorithm can achieve better performance than the conventional ANC algorithm especially when the secondary path changes drastically.

Study of non-linear filter-based algorithms for active noise control of machinary noises

Sometimes, machinary noise comprises of non-linear deterministic noise. Therefore, it is expected that for such cases of noises, non-linear filter based active noise control algorithms can provide improved performance in terms of reduction of noise. Various non-linear filter based algorithms explored in this paper are: Volterra filtered-x least mean square algorithm, Hammerstein algorithm, Fourier algorithm utilising Fourier non-linear filter, even mirror Fourier non-linear filter and CN filter, bilinear algorithm, Legendre algorithm, filtered-s least mean square using functional link artifical neural network (FLANN) filter, generalised FLANN, exponential FLANN and recursive FLANN. Noise from various workshop machines like band saw, compressor and welding process were recorded using a microphone and utilised to study the algorithms. Two cases of primary paths, (a) linear primary path and (b) non-linear primary path with cubic non-linearity, are considered for simulation study. Performance of various algorithms is evaluated based on steady-state residual noise.

Simultaneous perturbation algorithm for active noise control with an asynchronous wireless microphone

This paper investigates the issues on the use of an asynchronous wireless microphone in an active noise control (ANC) system. In a previous work, the jitter effect of the wireless error microphone is addressed by adopting the simultaneous variable perturbation (SVP) algorithm to update the control filter of the ANC system. However, another critical issue has been overlooked. The wireless microphone can have a different sampling rate from that of the ANC controller, because the analog-to-digital convertors for the ANC controller and the wireless microphone are triggered by separate crystal oscillators. This may deteriorate the convergence performance of the ANC system. In this paper, the simulation results demonstrate that both the SVP and online secondary path modeling (OSPM) algorithms are not affected by the sampling rate of the wireless error microphone, when the sampling rate difference is less than 0.01%. However, the sampling rate difference of the wireless reference microphone can lead to about 7 dB and 5 dB degradations in the noise reduction performance of the SVP and OSPM algorithms, respectively.

Study of machine learning based algorithms for active noise control of machinary noise

Machine learning is becoming a part of every field. After the discovery of deep learning, its application in various fields are increasing day by day. This paper presents a study of three machine learning based active noise control algorithms to reduce machinary noise. Three algorithms studied in this work are: filtered-x backpropagation neural network (FxBPNN), radial basis function (RBF) and deep recurrent neural network (DRNN). Experimentally recorded noises of band saw, CNC and compressor are used in the simulation study. Two cases of primary path are considered: a) linear path and b) nonlinear path, along with a linear secondary path. Performance of the three algorithms are evaluated based on steady state residual noise.

Application of Active Impulsive Noise Control (AINC) for Excavator Cabin Using Advanced Convex-Combined Normalized step size Algorithm and Verification of Robustness

Conventional Active Noise Control (ANC) algorithms, such as Filtered-x least mean square (FxLMS), is not suitable for Active Impulse Noise Control (AINC). Because it lacks robustness to the impulsive sample input, it is unstable and diverges very easily. Therefore, AINC algorithm must be robust to impulse. The heavy equipment such as excavators often occur frequent impulse noise during operations. Also, in working environment of operating excavator, impulsive noise is prone to occurred by other heavy equipment. Therefore, it is necessary to examine the AINC algorithm that is suitable for excavators and its robustness. In this study, Convex Combined Step Size (CCSS), which is simulated better effects in AINC than conventional ANC algorithms, is tried to AINC of excavator. In addition, the noise reduction performance and robustness to undesirable sound were verified in the simulated experiment environment of the excavator cabin.

Frequency domain adaptation of multichannel active road-noise sound control system with virtual sensing

The convergence speed of multichannel adaptive active noise control algorithms can be improved by pre-whitening the reference signals and reducing their cross-correlations. When considering nonstationary processes, like road noise, it proves advantageous to use a short time-based cross-correlation estimation of the reference signal, which can be obtained as part of the bin-normalised frequency domain least-means-squares algorithm. However, the regularisation of such an algorithm is crucial to its success and can further improve the convergence rate. In addition, virtual sensors can be employed by using the additional filter method, which provides targets for the measured error signals to follow, generated from the reference signals. Still, the performance of this virtual sensing is known to be degraded if the properties of the reference signals change, so special consideration should be taken when the reference signals are nonstationary.

Practical Considerations for Implementing Adaptive Acoustic Noise Cancellation in Commercial Earbuds

Active noise cancellation has become a prominent feature in contemporary in-ear personal audio devices. However, due to constraints related to component arrangement, power consumption, and manufacturing costs, most commercial products utilize fixed-type controller systems as the basis for their active noise control algorithms. These systems offer robust performance and a straightforward structure, which is achievable with cost-effective digital signal processors. Nonetheless, a major drawback of fixed-type controllers is their inability to adapt to changes in acoustic transfer paths, such as variations in earpiece fitting conditions. Therefore, adaptive-type active noise control systems that employ adaptive digital filters are considered as the alternative. To address the increasing system complexity, design concepts and implementation strategies are discussed with respect to actual hardware limitations. To illustrate these considerations, a case study showcasing the implementation of a filtered-x least mean square-based active noise control algorithm is presented. A commercial evaluation board accommodating a low-cost, fixed-point digital signal processor is used to simplify operation and provide programming access. The earbuds are obtained from a commercial product designed for noise cancellation. This study underscores the importance of addressing hardware constraints when implementing adaptive active noise cancellation, providing valuable insights for real-world applications.

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

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

An active noise control algorithm based on an improved convex combination loop

The conflict between a faster convergence using a Filtered-X Least Mean Square (FXLMS) algorithm and the requirement of a small steady-state error of an adaptive active control (ANC) system which limits its performance in terms of computation efficiency and the control error. The problem can be solved by using two paralleling adaptive filters to form a convex combination loop in the control system. However, the exponential weight function used in the original convex combination loop design can consume a large computer power which has limited its application. As a result, a new weight function is proposed to replace the original exponential function in the original convex combination loop to enhance the convergence rate of the adaptive ANC system. Furthermore, a MRFXLMS algorithm is used to replace the FXLMS algorithm in the ANC system so that it can be used for impulse noise control. The result shows that the proposed algorithm performs well in the control of input impulse noise with different intensity.

Directional active noise control for drones

Drones have become ubiquitous in various everyday applications, but their noise pollution remains an annoying issue. In this paper, we study the possibility of applying active noise control on drones with attached speakers. We propose the active noise control algorithms designed to iteratively calculate driving signals of speakers for canceling the outgoing noise from drones, specifically focusing on the blade passage frequencies within a directional spherical sector region. Two active noise control strategies are proposed: (i) minimizing the residual signal coefficients and (ii) minimizing the acoustic potential energy within the sector region. We derive update equations for two variables: (a) the loudspeaker weights and (b) secondary source spherical sector harmonics coefficients. Considering the power and loading limitations of drones, simulations are conducted under constraints of secondary source numbers to investigate the noise reduction performance with various secondary source setups. Simulation results demonstrate that the proposed method can effectively reduce the noise level over the directional sector region with a fast convergence speed.

Careful feedback active noise and vibration control algorithm robust to large secondary path changes

Feedback active noise and vibration control (ANVC) reduces narrowband noise and vibration when there is no reference signal. However, most ANVC algorithms require a secondary path (plant) model and can become unstable when this model is inaccurate. This work proposes an algorithm less sensitive to plant modeling errors and can cope with large and fast plant changes. This is achieved by modeling the plant and the noise using an autoregressive exogenous input (ARX) model. This model allows obtaining the expected values of the future residual noise and determining the optimal control signal. Since the model coefficient uncertainty (variance) is considered when calculating the expected value, the resulting controller is a careful controller. This allows solving problems due to significant model estimation errors when information is insufficient to estimate the full ARX model.

An Investigation of Real-Time Active Noise Control for 10 kV Substation Noise Suppression

Substation noise is a crucial factor that influences residents’ quality of life, especially in the densely residential areas. Despite small- and medium-sized transformer facilities having relatively low noise levels, due to their proximity to residential areas, they generate considerable annoyance, rendering them a focal point among environmental noise complaints. The predominant noise emitted by these facilities falls within the medium- and low-frequency spectrum range, and the conventional passive noise reduction techniques exhibit limited efficacy in attenuating such low-frequency noise. This study develops a real-time active noise control (ANC) system based on a digital signal processor, TMS320F28335, and various ANC methods, including Filtered-X Least Mean Squares (FxLMS), Normalized Filter-X Least Mean Squares (FxNLMS), and variable step-size FxLMS (VS-FxLMS), are evaluated for the low-frequency noise reduction. In addition, the substation noises at a residential community are measured, analyzed, and used as noise source together with a series of sinusoidal waves for evaluation of the ANC algorithms. Results show the ANC system are effective in attenuating most low-frequency noises (within 600 Hz) and the average noise reduction for the substation noises has achieved by more than 12 dB.

Reference Microphone Free Active Headrest System for Noise Containing Varying Frequency Tonal Components

In this brief, the design and implementation of an active headrest system are given. An efficient narrowband multichannel active noise control (ANC) algorithm known as filtered-e weighted-frequency Fourier linear combiner least mean square (FE-WFLC-LMS) is implemented in the proposed active headrest system to effectively control the undesired acoustic noise without hampering the desired sound such as speech and alarms. The computational complexity analysis along with simulation and real-time experiments are provided to demonstrate the efficiency of the proposed algorithm. Through simulation study, it is shown that the proposed multichannel FE-WFLC-LMS algorithm is capable of controlling the noise containing variable tonal frequencies effectively without use of reference microphone. In real-time experiments, it is observed that the proposed algorithm is able to track the frequency change to effectively control the noise without using any reference microphone.

A Complete Design of Smart Pad That Reduces Snoring

This paper presents integration of active noise control (ANC) systems with a smart pad to reduce the noise made by snoring. One reference microphone, two secondary speakers and two error microphones are used with a feedforward ANC algorithm in the smart pad design. Several smart functions including snore detection, quiet zone localization, and advanced audio integration are proposed. An effective way to satisfy the causality constraint of the feedforward ANC system is presented. The smart pad is independent of the pillow and is not in contact with the user; it is portable and is not a burden to the user during sleep. Several experiments are conducted to confirm the effectiveness of the smart pad when used on a full-size bed.

Selective fixed-filter active noise control based on frequency response matching in headphones

Most active noise control (ANC) headphones operate with a fixed-filter method. When the directions or spectral characteristics of the primary noise fluctuate significantly, the fixed filter exhibits an unsatisfactory noise reduction performance. To address this problem, this study proposes a selective fixed-filter ANC algorithm based on frequency response matching (FRM-SFANC), which chooses a pre-training filter to attenuate variable primary noise. An online modelling approach is adopted to achieve the FRM mechanism. Compared to the conventional fixed-filter method, FRM-SFANC provides superior and robust noise reduction performance.

Maximum versoria criterion applied to hybrid active noise control algorithm for the impulsive noise

In the conventional hybrid active noise control (CHANC) algorithm, the filtered-x least mean square (FXLMS) algorithm is used in both feedforward and feedback structures. However, the FXLMS algorithm does not process the reference signal and the error signal before updating the weight vector, which leads to the inadequate robustness of the CHANC algorithm in the impulsive noise environment. To solve this problem, the maximum versoria criterion is incorporated into the HANC (MVC-HANC) algorithm in this paper. Firstly, the MVC-HANC algorithm employs a novel nonlinear function to compress the error signal. Secondly, the proposed algorithm uses the modified sigmoid function to constrain the reference signal. To further improve the noise attenuation performance, the MVC-HANC algorithm uses a novel exponential function to adjust the step-size adaptively. Simulation and experiment results demonstrate that the proposed algorithm has the better attenuation performance than the conventional HANC algorithm.

Directional Suppression of Monotone Noises with A Parametric Array Loudspeaker

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

Active noise control with selective perceptual equalization to shape the residual sound

An active noise control (ANC) system usually contains a control filter to generate an anti-noise sound that destructively interferes with the primary noise. The filtered reference least mean squares (FxLMS) algorithm is the seminal adaptive ANC algorithm that updates the control filter coefficients iteratively. However, the FxLMS algorithm is primarily designed to minimize the power of the residual sound, without consideration to the auditory sensation. Thus, the residual sound can be perceived to be different from the primary noise of a broad frequency band. When an ANC system running the FxLMS algorithm was piloted in a substation’s control room, workers expressed discomfort with the residual sound, even though the noise level was reduced. It was mainly because the workers were annoyed by the residual sound which they identified as an anomaly operating state. In order to resolve this problem, this paper proposes a selective perceptual equalization FxLMS (SPE-FxLMS) algorithm to retain the operating state in the residual sound. The simulation, experiment and subjective test results validate the effectiveness of the SPE-FxLMS algorithm in reducing the noise level and shaping the residual sound to create a sense of auditory familiarity for the workers.