ANCシステムにおけるオンライン2次経路とフィードバック経路モデリングのための補助ノイズ電力スケジューリングに関する研究

The idea of cancelling the acoustic noise by generating an anti-noise signal is very fascinating, and was first proposed by P. Lueg in 1936. In feedforward active noise control (ANC) systems, the anti-noise signal is generated with the help of reference and error microphones, an adaptive filtered-x-LMS (FxLMS) algorithm based ANC filter, and an electro-acoustic path named as the secondary path. For stable operation of ANC systems, the FxLMS algorithm needs an estimate of the secondary path. The anti-noise signal generated by the loudspeaker (part of secondary path) causes interference with the reference microphone signal. This interference is due to the presence of electro-acoustic path, named as feedback path, between the loudspeaker and the reference microphone. It is required to neutralize the effect of this feedback path, and hence an estimate of the feedback path is required. For online modeling of the secondary and feedback paths, an additional auxiliary noise is injected. This auxiliary noise contributes to the residual error, and thus degrades the noise-reduction-performance (NRP) of ANC system. In order to improve the NRP, a gain scheduling strategy is used to vary the variance of the injected auxiliary noise. The purpose of the gain scheduling is that when the model estimates of the secondary and the feedback paths are far from the actual unknown paths, auxiliary noise with large variance is injected. Once the model estimates are closer to the actual unknown paths, the variance of auxiliary noise is reduced to a small value. In this way, on one hand the gain scheduling can help us to achieve the required model estimates of secondary and feedback paths, and on the other hand to improve the NRP at the steady-state. In this thesis, we discuss the two most important issues, i.e., 1) online secondary path modeling (OSPM), and 2) online feedback path modeling and neutralization (FBPMN) with gain scheduling. In chapter 1, the basic underlying physical principle and configurations of active noise control (ANC) systems are explained. The application of the basic building block of an ANC system i.e. An adaptive filter, in different system identification scenarios is discussed. The most popular adaptive algorithm for ANC system, i.e., FxLMS algorithm is derived for the general secondary path. A brief overview is given for the two fundamental issues in ANC systems, i.e., 1) OSPM and 2) online FBPMN. The use of optimal excitation signal, i.e., Perfect sweep signals for system identification is described. In chapter 2, the existing methods for OSPM without gain scheduling, where the auxiliary noise with fixed variance is used in all operating conditions, are discussed. In this chapter a simplified structure for OSPM with the modified FxLMS (MFxLMS) adaptive algorithm is proposed. The advantage of the simplified structure is that it reduces the computational complexity of the MFxLMS algorithm based OSPM without having any compromise on the performance of ANC system. In chapter 3, the existing methods for OSPM with gain scheduling are discussed. The drawbacks with the existing gain scheduling strategies are highlighted, and some new gain scheduling strategies are proposed to improve the modeling accuracy of SPM filter and the NRP of an ANC system. In existing methods, the gain is varied based on the power of residual error signal which carries information only about the convergence status of ANC system. In the Proposed methods the gain is varied based on the power of error signal of SPM filter. This is more desirable way of controlling the gain because the power of error signal of SPM filter carries information about the convergence status of both the ANC system and the SPM filter. The performance comparison is carried out through the simulation results. In chapter 4, the second most important issue associated with the feedforward configuration of ANC system, i.e., the issue of online FBPMN is deal with. In the first part, the existing methods for online FBPMN without gain scheduling are discussed. A new structure is proposed for online FBPMN without gain scheduling. The performance of the existing methods is compare with the proposed method through the simulation results. In the new structure the good features from the existing structures are combined together. The predictor is used in the new structure to remove the predictable interference term from the error signal of adaptive FBPMN filter. In addition to this, the action of FBPM filter and the FBPN filter is combined into a single FBPMN filter. The advantage of the new structure over the existing structures is that it can better neutralize the effect of feedback coupling on the input signal of ANC filter, thus improves the convergence of ANC system. In the second part, a gain scheduling strategy is proposed to improve the NRP of ANC system. In addition to this, a self-tuned ANP scheduling strategy with matching step-size for FBPMN filter is also proposed that requires no tuning parameters and further improves the NRP of ANC systems. In chapter 5, the concluding remarks and some future research directions are given.電気通信大学201

ACTIVE NOISE CONTROL USING CARBON NANOTUBE THERMOPHONES: CASE STUDY FOR AN AUTOMOTIVE HVAC APPLICATION

The goal of this project was to reduce the overall noise levels emitted by the HVAC components in a vehicle’s cabin. More specifically, the feasibility of achieving this goal using two key technologies was investigated. The first of these technologies, Active Noise Control (ANC), is a noise attenuation technique that relies on destructive interference that “cancels” unwanted noise. Typically used in situations where physical constraints prevent passive attenuation techniques from being used, ANC is known for its high size-to-effectiveness ratio. This benefit cannot be gained without a cost however; the complexity of ANC systems is significantly higher than their passive counterparts. This is due to the signal processing and actuator designs required. These actuators often take the form of moving-coil loudspeakers which, while effective, are often bulky. Because of this they are difficult to “drop in” to an existing system. This is where the second technology comes in. Carbon Nanotube (CNT) Thermophones are solid-state speakers that operate by using rapid heat fluctuations to create sound. Called the “thermoacoustic effect,” (TE) the theory of this operating principle dates to the turn of the 20th century. Useful demonstration of TE did not occur until 2008, however, when researchers first developed the first CNT thermophones. The hallmark characteristics of these transducers are their small size and flexible nature. Compared to traditional loudspeakers they have a much smaller form factor and are more versatile in terms of where they can be placed in a cramped system. The marriage of CNT transducers to ANC technology shows promise in improving the application space and ease of installation of ANC systems. Getting these two to cooperate, however, is not without challenges. A case study for this union is presented here; the application space being the ducted environment of vehicle HVAC systems

Analysis and implementation of active noise control strategies using Piezo and EAP actuators

Currently noise cancellation, which affects the lives of people and in the workplace is achieved through the active noise reduction. This measure is not expensive as passive or semi active measures also permits adequate air conduction in duct ventilation systems. The system control is achieved through a suitable location of the phase in the cancelling noise signal relative to the signal primary noise. Algorithms have been developed and strategies for active noise reduction and its implementation and experimental testing on duct ventilation. The actives elements used are Piezo Actuators and EAP as speakers; Individual and collective operation of the aforementioned actuators is examined. The work was evaluated as follows: Analysis of previous research on existing algorithms for active noise reduction. Study the strategies of simulation and implementation for active noise control algorithms designed.Tesi

Adaptive Algorithms Design for Active Noise Control Systems with Disturbance at Reference and Error Microphones

Active noise control (ANC) is a popular choice for mitigating the acoustic noise in the surrounding environment resulting from industrial and medical equipment, appliances, and consumer electronics. ANC cancels the low frequency acoustic noise by generating a cancelling sound from speakers. The speakers are triggered by noise control filters and produce sound waves with the same amplitude and inverted phase to the original sound. Noise control filters are updated by adaptive algorithms. Successful applications of this technology are available in headsets, earplugs, propeller aircraft, cars and mobile phones. Since multiple applications are running simultaneously, efficiency of the adaptive control algorithms in terms of implementation, computations and performance is critical to the performance of the ANC systems. The focus of the present project is on the development of efficient adaptive algorithms that perform optimally in different configurations of ANC systems suitable for real world applications.Thesis (Ph.D.) -- University of Adelaide, School of Electrical & Electronic Engineering, 202

Computationally efficient adaptive algorithms for active control systems

University of Technology Sydney. Faculty of Engineering and Information Technology.As the side effect of urbanization, acoustic noise from various sources is the most common health threat in our day-to-day life. Passive noise control methods are constrained by several factors such as frequency content of noise, absorbing material type, thickness and geometry. Alternatively, active noise control method has emerged as a promising solution to control low-frequency noise cost-effectively. In an active noise control system, the acoustic path from the control source to the error microphone affects the control performance. If the reference microphone is placed in close proximity of the control source, an unwanted acoustic feedback signal from the control source will be captured by the reference microphone, which may lead to system instability. Furthermore, the adaptive control algorithms have a high computational complexity, which limits its application with high sampling frequency and the scalability of a control system for generating a larger quiet zone. Various algorithms have been proposed in literature for modelling acoustic paths, low-complexity implementation of single and multiple channel control systems. However, they are still constrained by factors such as computational complexity, noise reduction performance, causality issue and stability issue. The objectives of this PhD research are to develop low-complexity algorithms for (1) online modelling of acoustic paths without affecting noise reduction performance, (2) achieving improved control performance at transient and steady state, (3) high sampling frequency operation and broadband noise control and (4) multiple channel decentralized algorithm for broadband noise control. […] In summary, online acoustic path modelling methods are proposed using the control signal; an affine combination of adaptive filters are proposed for improved control performance; a time-frequency domain flexible structure is proposed for active control operation for high sampling frequency operation; a decentralized algorithm is proposed to achieve similar noise reduction performance as the centralized one for broadband control

Development of Novel Techniques to Study Nonlinear Active Noise Control

Active noise control has been a field of growing interest over the past few decades. The challenges thrown by active noise control have attracted the notice of the scientific community to engage them in intense level of research. Cancellation of acoustic noise electronically in a simple and efficient way is the vital merit of the active noise control system. A detailed study about existing strategies for active noise control has been undertaken in the present work. This study has given an insight regarding various factors influencing performance of modern active noise control systems. The development of new training algorithms and structures for active noise control are active fields of research which are exploiting the benefits of different signal processing and soft- computing techniques. The nonlinearity contributed by environment and various components of active noise control system greatly affects the ultimate performance of an active noise canceller. This fact motivated to pursue the research work in developing novel architectures and algorithms to address the issues of nonlinear active noise control. One of the primary focus of the work is the application of artificial neural network to effectively combat the problem of active noise control. This is because artificial neural networks are inherently nonlinear processors and possesses capabilities of universal approximation and thus are well suited to exhibit high performance when used in nonlinear active noise control. The present work contributed significantly in designing efficient nonlinear active noise canceller based on neural network platform. Novel neural filtered-x least mean square and neural filtered-e least mean square algorithms are proposed for nonlinear active noise control taking into consideration the nonlinear secondary path. Employing Legendre neural network led the development of a set new adaptive algorithms such as Legendre filtered-x least mean square, Legendre vi filtered-e least mean square, Legendre filtered-x recursive least square and fast Legendre filtered-x least mean square algorithms. The proposed algorithms outperformed the existing standard algorithms for nonlinear active noise control in terms of steady state mean square error with reduced computational complexity. Efficient frequency domain implementation of some the proposed algorithms have been undertaken to exploit its benefits. Exhaustive simulation studies carried out have established the efficacy of the proposed architectures and algorithms

Control of feedback for assistive listening devices

Acoustic feedback refers to the undesired acoustic coupling between the loudspeaker and microphone in hearing aids. This feedback channel poses limitations to the normal operation of hearing aids under varying acoustic scenarios. This work makes contributions to improve the performance of adaptive feedback cancellation techniques and speech quality in hearing aids. For this purpose a two microphone approach is proposed and analysed; and probe signal injection methods are also investigated and improved upon