Nonlinear active noise control with virtual sensing technique

IEEE International Conference on Engineering of Intelligent Systems, 2006

This paper proposes a new method for online modeling of the acoustic feedback path in active noise control (ANC) systems. The proposed method comprises three adaptive filters: 1) the FxLMS algorithm based noise control filter, 2) a variable step size (VSS) LMS algorithm based feedback path modeling (FBPM) filter, and 3) LMS algorithm based adaptive noise cancellation (ADNC) filter. The

2001

Traditional active noise control systems achieve the greatest noise reduction at the locations of the error sensor(s). In many cases it is desirable to be able to achieve the maximum noise reduction remote from an error sensor. One way of doing this is to measure the transfer function between the desired location of maximum reduction and the error sensor and incorporate it in the control algorithm. The disadvantage of this method is that it is not robust to changes in the acoustic environment. Another method relies on using two or more microphones to estimate the sound level at a remote location using forward prediction. This method results in a lower performance but it can be adapted to changes in the acoustic environment as well as to changes in the location of the desired pressure minimum. This paper will report on a study that compares the relative merits of various forward prediction method in various situations. These commence with a free field environment (to introduce the concept) and then progress to a more practical application of an aircraft cabin. Single and multiple control sources will be considered as will sound pressure sensing and energy density sensing.

The Journal of the …, 2009

IEEE Access

The conventional adaptive noise control algorithms create a zone of quiet at the location of an error microphone. Several virtual active noise control (ANC) algorithms have been suggested in the literature to introduce a more flexible positioning of the zone of quiet. The main objective of the proposed ANC system in our research is to minimize the noise disturbance on human hearing by considering the auditory system characteristics. We have introduced the human perception of acoustic disturbances in our ANC modeling. This paper presents a novel approach based on a psychoacoustically enabled remote ANC algorithm to minimize the acoustic noise at a zone away from the error microphone location. This approach depends on modeling the transfer function between the error microphone and the remote zone of quiet. The developed model has been implemented using an FPGA real-time module, and the performance is validated at four different remote locations. Noise-weighting filters are incorporated to observe the psychoacoustic characteristics of the proposed model. In the experimental setup, the acoustic noise at the physical and remote locations are monitored to quantify the noise reduction characteristics of the proposed algorithm. The simulation and experimental results show a noise reduction of 15-18 dBs has been achieved, which is an average improvement of 5-8 dBs over the standard ANC model.

… on Sound and …, 2007

Local active noise control systems can be used to generate a zone of quiet at a physical error sensor using one or more secondary sources to cancel the acoustic pressure and its spatial derivatives at the sensor location. The resulting zone of quiet is generally limited in size and as such, placement of the physical error sensor at the location of desired attenuation is required, which is often inconvenient. Virtual acoustic sensors overcome this by projecting the zone of quiet away from the physical error sensor to a remote location. The work described here investigates the effectiveness of using virtual microphones and virtual acoustic energy density sensors in a diffuse sound field. Expressions for the performance of the virtual microphones and virtual acoustic energy density sensors have been developed using the forward-difference extrapolation technique which has been rederived for use in diffuse sound fields. Results from simulations will be presented, together with experimental results obtained in a reverberant chamber.

Local active noise control systems aim to produce zones of quiet at a number of desired locations within a sound field, such as the ears of an observer. The resulting zones of quiet are usually centered at the error sensors, and are often too small to extend from the error sensors to the observer's ears. To overcome these problems, virtual sensing methods have been suggested. These methods are based on estimating the error signals at a number of locations remote from the physical locations of the error sensors. By minimising the estimated error signals, the zones of quiet can be moved away from the error sensors to the locations where noise control is desired, i.e. the virtual locations. In this paper, the active noise control problem under consideration is analysed using a state-space model of the plant. Kalman filtering theory is then used to develop a virtual sensing algorithm that computes optimal estimates of the error signals at the virtual locations. The developed algorithm is implemented on an acoustic duct arrangement, and the real-time estimation performance at a virtual location inside the acoustic duct is analysed. Furthermore, the developed algorithm is combined with the filtered-x LMS, and the results of real-time broadband feedforward control experiments at the virtual location are presented.

IEEE Transactions on Audio, Speech and Language Processing, 2000

The presence of strong acoustic feedback degrades the convergence speed of the active noise control (ANC) filter, and in the worst case the ANC system may become unstable. A fixed feedback neutralization filter, obtained offline, can be used to neutralize the acoustic feedback. The feedback path, however, may be time varying, and we may need continual adjustments during online operation of the ANC system. This paper proposes a new method for online modeling of the acoustic feedback path in ANC systems. The proposed method uses three adaptive filters; a noise control filter, a feedback path modeling (FBPM) filter, and an adaptive noise cancelation (ADNC) filter. The objective of ADNC filter is to remove the disturbance from the desired response of FBPM filter. In comparison with the existing method, which works only for predicable noise sources, the proposed method can work, as well, with the broadband noise sources. The computer simulations are carried out for narrowband (predictable) (case I) and broadband (random) noise sources (case II). It is demonstrated that the proposed method performs better than the existing method in both cases.

Proceedings of Active, 2002