Active noise control on high frequency narrow band dental drill noise: Preliminary results

Dental drills produce a characteristic noise that is uncomfortable for patients and is also known to be harmful to dentists under prolonged exposure. It is therefore desirable to protect the patient and dentist whilst allowing two-way communication. A solution is to use a combination of the three main noise cancellation methods, namely, Passive Noise Control, Adaptive Filtering and Active Noise Control. Dental drill noise occurs at very high frequency ranges in relation to conventional ANC, typically 2kHz to 6kHz and it has a narrow band characteristic due to the direct relation of the noise to the rotational speed of the bearing. This paper presents a design of an experimental rig where first applications of ANC on dental drill noise are executed using the standard filtered reference Least Mean Square (FXLMS) algorithm. The secondary path is kept as simple as possible, due to the high frequency range of interest, and hence is chosen as the space between headphone loudspeaker and error microphone placed in the ear (input of the headphone loudspeaker and the output of the error microphone). A standard headphone loudspeaker is used for the control source and the microphone inside of an “Ear and Cheek Simulator Type 43AG” is used as the error microphone. The secondary path transfer function is obtained and preliminary results of the application of ANC are discussed

Active Noise Control with Sampled-Data Filtered-x Adaptive Algorithm

Analysis and design of filtered-x adaptive algorithms are conventionally done by assuming that the transfer function in the secondary path is a discrete-time system. However, in real systems such as active noise control, the secondary path is a continuous-time system. Therefore, such a system should be analyzed and designed as a hybrid system including discrete- and continuous- time systems and AD/DA devices. In this article, we propose a hybrid design taking account of continuous-time behavior of the secondary path via lifting (continuous-time polyphase decomposition) technique in sampled-data control theory

Blockwise Subspace Identification for Active Noise Control

In this paper, a subspace identification solution is provided for active noise control (ANC) problems. The solution is related to so-called block updating methods, where instead of updating the (feedforward) controller on a sample by sample base, it is updated each time based on a block of N samples. The use of the subspace identification based ANC methods enables non-iterative derivation and updating of MIMO compact state space models for the controller. The robustness property of subspace identification methods forms the basis of an accurate model updating mechanism, using small size data batches. The design of a feedforward controller via the proposed approach is illustrated for an acoustic duct benchmark problem, supplied by TNO Institute of Applied Physics (TNO-TPD), the Netherlands. We also show how to cope with intrinsic feedback. A comparison study with various ANC schemes, such as block filtered-U, demonstrates the increased robustness of a subspace derived controlle

Combined MIMO adaptive and decentralized controllers for broadband active noise and vibration control

Recent implementations of multiple-input multiple-output adaptive controllers for reduction of broadband noise and vibrations provide considerably improved performance over traditional adaptive algorithms. The most significant performance improvements are in terms of speed of convergence, the \ud amount of reduction, and stability of the algorithm. Nevertheless, if the error in the model of the relevant transfer functions becomes too large then the system may become unstable or lose performance. On-line adaptation of the model is possible in principle but, for rapid changes in the model, necessitates \ud a large amount of additional noise to be injected in the system. It has been known for decades that a combination of high-authority control (HAC) and low-authority control (LAC) could lead to improvements with respect to parametric uncertainties and unmodeled dynamics. In this paper a full digital implementation of such a control system is presented in which the HAC (adaptive MIMO control) is implemented on a CPU and in which the LAC (decentralized control) is implemented on a high-speed Field Programmable Gate Array. Experimental results are given in which it is demonstrated that the HAC/LAC combination leads to performance advantages in terms of stabilization under parametric uncertainties and reduction of the error signal

Active disturbance cancellation in nonlinear dynamical systems using neural networks

A proposal for the use of a time delay CMAC neural network for disturbance cancellation in nonlinear dynamical systems is presented. Appropriate modifications to the CMAC training algorithm are derived which allow convergent adaptation for a variety of secondary signal paths. Analytical bounds on the maximum learning gain are presented which guarantee convergence of the algorithm and provide insight into the necessary reduction in learning gain as a function of the system parameters. Effectiveness of the algorithm is evaluated through mathematical analysis, simulation studies, and experimental application of the technique on an acoustic duct laboratory model

A Novel Method for Acoustic Noise Cancellation

Over the last several years Acoustic Noise Cancellation (ANC) has been an active area of research and various adaptive techniques have been implemented to achieve a better online acoustic noise cancellation scheme. Here we introduce the various adaptive techniques applied to ANC viz. the LMS algorithm, the Filtered-X LMS algorithm, the Filtered-S LMS algorithm and the Volterra Filtered-X LMS algorithm and try to understand their performance through various simulations. We then take up the problem of cancellation of external acoustic feedback in hearing aid. We provide three different models to achieve the feedback cancellation. These are - the adaptive FIR Filtered-X LMS, the adaptive IIR LMS and the adaptive IIR PSO models for external feedback cancellation. Finally we come up with a comparative study of the performance of these models based on the normalized mean square error minimization provided by each of these feedback cancellation schemes

An Efficient & Less Complex Solution to Mitigate Impulsive Noise in Multi-Channel Feed-Forward ANC System with Online Secondary Path Modeling (OSPM)

This paper deals with impulsive noise (IN) in multichannel (MC) Active Noise Control (ANC) Systems with Online Secondary Path Modelling (OSPM) employing adaptive algorithms for the first time. It compares performance of various existing techniques belonging to varied computational complexity range and proposes four new methods, namely: FxRLS-VSSLMS, VSSLMS-VSSLMS, FxLMAT-VSSLMS and NSS MFxLMAT-VSSLMS to deal with modest to very high impulsive noise (IN). Simulation results show that these proposed methods demonstrated improved performance in terms of fast convergence speed, lowest steady state error, robustness and stability under impulsive environment in addition to modelling accuracy for stationary as well as non-stationary environment besides reducing computational complexity many folds