Design of Adaptive Exponential Functional Link Network-Based Nonlinear Filters

In practical active noise control (ANC) systems, the primary path and the secondary path may be nonlinear and time-varying. It has been reported that the linear techniques used to control such ANC systems exhibit degradation in performance. In addition, the actuators of an ANC system very often have nonminimum-phase response. A linear controller under such situations yields poor performance. A novel functional link artificial neural network (FLANN)-based simultaneous perturbation stochastic approximation (SPSA) algorithm, which functions as a nonlinear mode-free (MF) controller, is proposed in this paper. Computer simulations have been carried out to demonstrate that the proposed algorithm outperforms the standard filtered-x least mean square (FXLMS) algorithm, and performs better than the recently proposed filtered-s least mean square (FSLMS) algorithm when the secondary path is time-varying. This observation implies that the SPSA-based MF controller can eliminate the need of the modeling of the secondary path for the ANC system.

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

Based on the theoretical analysis of literature, saturation in measured signal of active noise control (ANC) systems will degrade the convergence speed. However, the experiments show that the saturated input signal can speed up the convergence of the narrow-band ANC systems. This paper intends to remodel the saturation effects for feedforward and feedback ANC systems. Combining the action of analog-to-digital converters (ADC), the mathematical expression and block diagrams are proposed to model the saturation effects in the practical ANC systems. The derivation and simulation results show that since the saturation is able to amplify the principle component of signal, the convergence would be speeded up.

The objective of this research project is to develop an Active Noise Control (ANC) system based on the Filtered-X Least Mean Square algorithm. The ANC system developed here will be installed in a rocket engine testing facility of SpaceX Company near McGregor, Texas. As the SpaceX Company’s rocket engines have been increased in their powers, the engine noise from the testing facility is increasing rapidly. Although there is no noise complain currently from the nearby communities in McGregor, concerns are expected in the near future that the noise could possibly disturb people in McGregor. The ANC system can reduce the noise level using a few microphones and speakers connected to a DSP frontend, while a Passive Noise Control system would be much larger and require a higher cost to be implemented. This ANC system is currently implemented as a Single Input and Single Output system in a laboratory, effectively reducing the noise generated from a loudspeaker driven by the recorded rocket engine noise signal. Then, the system will be improved and operated as a Multiple Inputs and Multiple Outputs system to successfully adapt various wind, temperature, and humidity changes, leading to the maximum noise reduction toward McGregor.

Excessive noise levels inside infants incubators in neonatal intensive care units (NICU) contribute to number of harmful effects on the infant's health. This paper develops and implements practical active noise control (ANC) systems for the infant incubators. The filtered-X least mean square (FXLMS) algorithm is used to cancel the noise inside the incubator. The multi-channel and pseudo multi-channel ANC systems are proposed to enhance the noise cancellation performance in terms of cancellation gain and quiet zone. An experimental setup based on real Giraffe incubator from GE Healthcare is used for real-time experiments. The results show that the ANC system can dramatically reduce the noise and cost effective.

Development of equation-error adaptive IIR-filter-based active noise control system

Multichannel feedforward active noise control system combined with noise source separation by microphone arrays

This paper presents an algorithm structure for an active noise control (ANC) system based on an improved equation error (EE) model that employs the offline secondary path modeling method. The noise of a compressor in a gas station is taken as an example to verify the performance of the proposed ANC system. The results show that the proposed ANC system improves the noise reduction performance and convergence speed compared with other typical ANC systems. In particular, it achieves 28 dBA noise attenuation at a frequency of about 250 Hz and a mean square error (MSE) of about −20 dB.

It is important to remove large environmental noise in measurement using SQUIDs without magnetic shielding. Here, the environmental noise has been reduced by an active noise control (ANC) system in the MHz radio-frequency region. The ANC system including a phase and amplitude control circuit has been designed to allow precise adjustments of the phase and the amplitude of the anti-phase wave against the noise waves in the MHz region. In this paper, a sinusoidal wave in the MHz range was used as the radio frequency interference. When a SQUID was used for a signal detector, the ANC system suppressed this sinusoidal wave to the white noise level by about 54 dB. The performance of the ANC system for the chemical detection by nuclear quadrupole resonance (NQR) was checked in a simulated environment. One was the case of a real AM radio broadcasting signal with the dummy NQR signal and another was the real NQR signal with a sinusoidal wave as noise. For both cases, the ANC showed good performance for MHz range noise cancellation.

Adaptive RSOV filter using the FELMS algorithm for nonlinear active noise control systems

A psychoacoustic active noise control (ANC) system based on empirical mode decomposition (EMD) is proposed and implemented to improve the noise reduction performance of the control system. The noise source signal is decomposed by EMD, and the psychoacoustic parameter â–œloudnessâ–? of each intrinsic mode function (IMF) is initially calculated in such a system. Thereafter, the high-pass psychoacoustic weighting filter used to shape the error and reference signals is designed adaptively and automatically according to the loudness, peak frequency, and amplitude of each IMF. Three different ANC systems are simulated, and the sound pressure levels and loudness of their residual error signals are compared. The results demonstrate that the filter designed using this method can restrain the components of noise sources with small loudness better than the A-weighting shaping filter, so that the proposed control system can improve the noise reduction compared to those of the filtered-x least mean square and A-weighting shaping filters. Finally, the computational complexity of the three ANC systems is analyzed and compared.

Interior Acoustic Control System Using Boundary Vibration with Giant Magnetostrictive Actuator: Fundamental Consideration on the Control Algorithm

Infant incubators are widely used in neonatal intensive care units (NICU) for the pre-term or sick babies. In this paper, we propose an active noise control (ANC) system integrated with infant cry detection and classification function for infant incubators. The incubator ANC system reduces the high noise level which results in numerous adverse health effects for infants, and the cry detector and analyzer monitors the infants' physical conditions using the same ANC system hardware. The infant crying signals picked up by the error microphone of ANC inside the incubator are detected, the signals' features are extracted by using linear prediction coding (LPC), and Mel-frequency cepstral coefficients (MFCC) methods. The database of different signals and their associated infant health features will be established through training phase. The signal classification algorithm is based on shortest distance and Bayesian classifier. Simulation is conducted to evaluate the performance of proposed system using a Giraffe incubator from GE Healthcare. The experiments show that the proposed ANC system can reduce the harmful noise and the cry classification algorithm can recognize infant cries in order to develop the innovative, low cost methods to monitor the infant's health conditions.

Electronic delay has been a critical problem in active noise control (ANC) systems. This is true whether a feedforward structure or a feedback structure is adopted. In particular, excessive delays would create a causality problem in a feedforward ANC system of a finite-length duct. This paper suggests a multirate signal-processing approach for minimizing the electronic delay in the control loop. In this approach, digital controllers are required in decimation and interpolation of discrete-time signals. The computation efficiency is further enhanced by a polyphase method, where the phases of low-pass finite impulse response (FIR) filters must be carefully designed to avoid unnecessary delays. Frequency domain optimization procedures based on H1, H2, and Hinfinity norms, respectively, are utilized in the FIR filter design. The proposed method was implemented by using a floating-point digital signal processor. Experimental results showed that the multirate approach remains effective for suppressing a broadband (200-600 Hz) noise in a duct with a minimum upstream measurement microphone placement of 20 cm.

In conventional active noise control (ANC) system, the primary noise is attenuated over the frequency band of interest based simply on the error signal, which does not take into account the human perception of noise. Hence, researchers have developed psychoacoustic ANC system to improve its noise reduction performance from psychoacoustic point of view. However, in this psychoacoustic ANC system, there may be a disturbance that is uncorrelated with the primary noise at the error sensor, which can severely degrade the system's noise reduction performance. Hence, in this paper, a psychoacoustic hybrid ANC system is proposed, which can simultaneously control both the correlated primary noise and uncorrelated disturbance from psychoacoustic point of view. Loudness is used as the psychoacoustic criterion for evaluating the system's noise reduction performance. Simulation results show the effectiveness of the proposed psychoacoustic hybrid ANC system.