Performing Active Noise Control and Acoustic Experiments Remotely

Commercial active noise and vibration control systems for vehicles are gradually developing, mainly in Japan, for both active noise control of interior sound and active control engine mounts [1]. There have been four Japanese commercial applications: • Active control for booming noise by Nissan in 1991, • Active vibration mount for idle noise and vibration by Toyota in 1997, • Active vibration mount for diesel engines by Nissan in 1998, • Active control for low-frequency road noise by Honda in 2000. Other manufacturers have, of course, developed similar systems, but these have not progressed to production applications. Additionally, active control engine mufflers using cancelling loudspeakers have been successfully demonstrated. A new type of active muffler, using a controlled oscillating butterfly valve in the exhaust line has been developed recently. The future of active control in vehicles is dependent on customers who are willing to pay the higher price for additional noise and vibration control, so that cost reductions are the essential factor in further commercial products. Trends towards fuel efficiency and lighter vehicles run counter to reducing noise and vibration. This has given new interest in active noise control and active control engine mounts as a solution to these conflicting issues.

The purpose of this chapter is to apply robust identification and control techniques to perform active noise control (ANC). The identification phase is based on a control-oriented robust identification approach that considers both parametric and nonparametric descriptions of the system, and quantifies the uncertainty. The controller design compares the feedback (FB), feedforward (FF) and hybrid (FB/FF) control structures. The feedback control is synthesized and evaluated in the robust control framework. It is designed using \( \mathcal{H}_\infty \) optimal control as a mixed-sensitivity problem, and the robust performance of the loop is analyzed via the structured singular value (μ). The FF controller is an adaptive identifier, based on the robustly normalized σ-algorithm. Two approaches are developed to decide what control structure is more efficient on a 4-m duct example with broadband noise. In addition, the compromises between identification and control, the inherent limitations of feedback and implementation issues in ANC are explicitly pointed out. Relations between performance, controller order, parametric/nonparametric models and digital signal processor (DSP) implementation are discussed. Theoretical and experimental results on the duct are compared. Finally the gaps that still remain between theory and practice in this type of applications, are outlined.

This paper describes the improvement of the active control of machinery noise to comfortable sound referred to insect's singing. In order to increase the resolution of comfortable functions, continuous acoustic psychological experiment is carried out. It is then confirmed that the results obtained by the experiment are correlated with the result obtained by the conventional experiment. Through the many experiment for a certain tester, it is confirmed that there is optimal Chirp period to maximize comfortableness. The active control experiment with the optimal Chirp period shows that the optimal Chirp period is effective.

The present work describes some of the research effort on Active Noise Control (ANC) being jointly developed by the Catholic University of Minas Gerais (PUC-MINAS) and the Federal University of Minas Gerais (UFMG). Considerations about the implementation of Digital Signal Processing for noise control in ducts has been presented. The objective is to establish a study on Active Noise Control in ducts combining geometry and acoustic parameters modification together with adaptive digital filtering implementation. Both algorithm and digital signal processing details are also discussed. The main results for a typical application where real attenuation has been obtained are presented and considered according to their use in developing real applications. The authors also believe that the present text should provide an interesting overview for both designers and students concerned about Active Noise Control in ducts. (To be presented in Portuguese.)

A tunable active-noise-control (ANC) circuit topology for headsets used in different applications is presented in this article. In the current consumer headset market, ANC is a mature technology that is commonly applied to wireless headsets connected to smartphones for listening to music and making phone calls. The development of ANC headsets has resulted in low-cost and simple devices due to the built-in ANC registers in the digital circuit. Digital circuit implementation is important to provide ultra-low latency processing for different algorithms compared with using digital signal processors (DSPs). However, a limitation associated with these built-in ANC filters is that the ANC digital circuit has been designed only for the wireless headset consumer market. Consequently, ANC headsets can only be used for designated features. ANC tuned for planes by eliminating low-frequency engine noise has been commonly used in the past twenty years. Pass-through (PT) amplification is also commonly tuned to allow users to hear external sounds and have conversations without having to remove their headset. In addition, ANC and PT amplification are also not allowed to operate simultaneously on the existing chipsets. In this paper, an ANC circuit topology with ultra-low latency processing is presented, where the ANC response of the headset can be tuned without any restriction imposed by the built-in ANC filters. The proposed ANC circuit topology consists of a commercial ANC chipset with an external audio codec to provide tunable characteristics. It is achieved by fixed maximum noise cancellation of ANC chipset being modified by summing with the signal from audio-codec path via an audio mixer. Acoustic performance for several headset applications implemented using the proposed circuit is validated. This simple circuit topology can be easily developed and adapted to the current headset consumer market for consumer-selective ANC in different environments.

Hybrid noise control is the combination of both active noise and passive noise control. For high-frequency noise, we generally use passive noise control technique, and for low-frequency noise of less than 200 Hz, we use active noise control technique. Passive noise control is achieved by introducing passive techniques such as enclosures, physical barriers, and silencers to attenuate the undesired noise, whereas active noise control (ANC) is achieved by introducing a cancelling “anti-noise” signal generated by a processor or electronics circuit. Digital filters can be used for this purpose with an appropriate adaptive algorithms such as FXLMS, FXNLMS, FBLMS, SE-FXLMS, and FULMS. The development of an ANC simulation model can be divided into two parts: the modelling of primary noise and the modelling of secondary or counter noise. The anti-noise generated by the ANC system can be modelled only if we have the information of primary noise. For successful simulation of secondary noise, we have to model the control system and its interaction with the surrounding acoustical space. This article describes the practical application of active noise control as well as passive noise control to a server room, greatly reducing the level of noise in the whole area through appropriate adaptive algorithms. The results obtained using different algorithms are compared and analyzed.

Active control for vehicle interior noise based on DWT-FxLMS algorithm using a piezoelectric feedback system

Fuzzy active noise modeling and control

An active noise control (ANC) system generally uses speakers and microphones as inputs and outputs. The ANC is effective in regions around the error microphones, known as the zones of quiet. In this paper, a linear matrix inequality (LMI) based robust control method is used to enlarge the zones of quiet around the microphones. This is achieved by considering microphones to have fictitious uncertain locations around their nominal positions, and by designing a controller to guarantee robustness against all these location-based uncertainties. As a result, noise reduction in a wider region is achieved. The control is developed based on a non-minimal state-space realization (NSSR) scheme that allows one to generate an equivalent set of states for the ANC system, using only the measured inputs and outputs, without any differentiation. Actual hardware experiments using a digital signal processor (DSP) have been carried out to show the applicability of the method.

With the advancement of digital signal technology, active noise control technique has gradually received more attention for the low-frequency noise in the vehicle. The active noise control apparatus and method have shortcomings in the theory and algorithm. Both acoustic devices researched on meeting the application requirements and adaptive active noise control algorithms are effective ways to improve the practicability of active noise control technique. The paper reviews various vehicle interior active noise control apparatuses in recent years. It has been found that the vehicle interior active noise control apparatuses can be classified as the extracting signal sources and the control strategy between cancelling signal and feedback signal. From review of the patents presented, it is found that there are many patents covering the active noise control method inside vehicle. However, there is a limitation in controlling speed and precision. Therefore, it is recommended that the development should be focused on the design of the control section of vehicle interior noise, which will improve the sound quality and save the space. Moreover, the cost of the new apparatus will be reduced by introducing intelligent controller. Keywords: Acoustic devices, active noise control strategy, algorithm, cancelling signal, feedback signal, vehicle interior noise.

Annoying noise in the passenger cabins of propeller aircraft, the rumble in air-conditioning systems, and the sounds disrupting headset communication are being reduced these days by active noise control, thanks to advances in digital signal processing. The technique relies on the principle of destructive interference between two sound fields, one field is generated by the original or primary sound source, the other by a secondary sound source setup to interfere with, and cancel, that unwanted primary sound. The primary source may be an engine and the secondary source, a loudspeaker with an electronically controlled output. Destructive interference is at its most efficient when the two sound fields can be accurately aligned in space over an acoustic wavelength. It works best on low frequency sounds, whose acoustic wavelengths are large compared to the zone in which the noise is cancelled.

This research developed active noise control (ANC) system for in-ear headphones based on the feedforward algorithm to reduce broadband noise. The setup of the designed ANC in-ear headphones contains a microphone that receives external noise and a digital signal processor as the computing core to generate the anti-noise. This research analyzed the noise attenuation performance of the feedforward ANC system with noise from different direction-of-arrival (DOA). Finally, the experimental results showed that the attenuation performance of the feedforward controller depends on the DOA of the noise. Moreover, we compared the proposed in-ear headphones with commercially available ones to verify their effectiveness in an anechoic chamber.

A number of projects have been carried out in Nanjing University to implement active noise control technologies, which include active control of transformer noise, active sound barrier, active noise control in communication chassis, active noise control in natural ventilation windows, active control of large impulsive noise in headset and active noise control in a train compartment. Unlike our previous research, these projects are all funded by industries and the aim is not for doing academic research, but to make commercial prototypes. Various challenges in the implementation of active noise control technologies in these projects to make commercial products are reported and discussed, and main issues to make successful commercial active noise control products are pointed out.

Active control of sound through full-sized open windows

Nowadays, there are many commercial ANC (Active Noise Control) headphones using either digital signal processors (DSPs) as control unit to implement the feedback FXLMS algorithm, or analog circuit by using control theory's feedback structure to cancel the noise in active way. However, for DSP's product, it's much expensive than conventional passive headphone. As a result, this paper proposed using a micro-controller unit (MCU), Cortex-M4, instead of DSP to implement the adaptive algorithm in headsets. Experimental results illustrate the digital control ANC headsets' performance with commercial headset to verify effectiveness.