Active noise control

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Analysis of Effort Constraint Algorithm in Active Noise Control Systems

Analysis of Effort Constraint Algorithm in Active Noise Control Systems

Adaptive algorithms are widely used for feed-forward con- trol systems, in which the mean-square error is minimized using the method of steepest descent, with no constraint on the magnitude of the control signals. In recent years, adap- tive signal processing has been developed and applied to the expanding field of active noise control (ANC) [1]. ANC is achieved by introducing a canceling antinoise wave through an appropriate secondary source as shown in Figure 1. These secondary sources are interconnected through an electric sys- tem using a specific signal processing algorithm for the par- ticular cancellation scheme [2].
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Performance and analysis of active noise control system for noise reduction in hearing aids

Performance and analysis of active noise control system for noise reduction in hearing aids

The adaptive feedback ANC method has a similar structure to the feed forward filter-X LMS algorithm. In order to compensate for the effects of the secondary path from the input of the secondary loudspeaker to the output of the error microphone, before being used to adjust the weights of controller, the reference signal is filtered by an estimated impulse response of the secondary path [12]. This leads to the filtered-x LMS (FxLMS) algorithm. The work also proposes a new algorithm to alleviate the effect of measurement noise on feedback ANC system. The proposed algorithm estimates the frequencies of the multi-tonal noise and generates an enhanced version of it. This enhanced signal is used as reference signal in the conventional feed forward ANC configuration. The performance comparison of the feedback system with an equivalent feed forward system is presented. In the context of the present investigation, the discrete cosine transform filter is used in a filter-X LMS implementation of a feedback active noise control system that uses a single error microphone and a single loudspeaker. If the effect of the secondary path is significant, the filtered-X LMS (FXLMS) algorithm is usually employed. The FXLMS algorithm is employed for the case of one real sinusoid, and the effect of the secondary path to the pass band characteristic of the ANC system is analyzed [13].
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An active noise control algorithm with gain and power constraints on the adaptive filter

An active noise control algorithm with gain and power constraints on the adaptive filter

This article develops a new adaptive filter algorithm intended for use in active noise control systems where it is required to place gain or power constraints on the filter output to prevent overdriving the transducer, or to maintain a specified system power budget. When the frequency-domain version of the least-mean-square algorithm is used for the adaptive filter, this limiting can be done directly in the frequency domain, allowing the adaptive filter response to be reduced in frequency regions of constraint violation, with minimal effect at other frequencies. We present the development of a new adaptive filter algorithm that uses a penalty function formulation to place multiple constraints on the filter directly in the frequency domain. The new algorithm performs better than existing ones in terms of improved convergence rate and frequency-selective limiting.
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Simulation Study of Active Noise Control in Wind Turbines Using FxLMS Adaptation Algorithm

Simulation Study of Active Noise Control in Wind Turbines Using FxLMS Adaptation Algorithm

Utility scale wind turbines produce a significant amount of noise which has been identified as one of the most critical challenges to the widespread use of wind energy. Aerodynamic noise caused primarily by the interaction of the boundary layer and (or) the upstream atmospheric turbulence with the trail- ing edge of the blade has been identified as the most dominant source of noise in wind turbines. The authors here propose an active noise control system based on the FxLMS algorithm which can achieve suppression of noise from a modern wind turbine. Two types of noise sources have been simulated: mo- nopole and dipole. The results of the active noise control algorithm are vali- dated with simulations in MATLAB. The agreement between the results shows the far impact of active noise control techniques will have in future wind turbines.
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Theory and Design of Spatial Active Noise Control Systems

Theory and Design of Spatial Active Noise Control Systems

The most commonly seen application of the active noise control technique is the active noise cancelling (ANC) headphones (Fig. 1.3). The ANC headphones typically employ a reference microphone, mounted on the outer surface of the head- phone’s housing. The reference microphone picks up the ambient noise, and sends the noise signal to a processing unit, which generates the anti-noise signals and plays it through the headphone driver along with the music signal [3]. In some designs, an additional error microphone is placed inside the ear cup to monitor the residual noise. It is also possible to use a feedback ANC structure, where the ref- erence microphone is omitted, one such design is detailed in [3]. Noise cancelling headphones can yield reasonably good noise attenuation, partially due to the fact that the secondary loudspeaker and the error microphone are placed very close to the ear. According to [4], significant attenuation of sinusoidal noise signal can be achieved for frequencies up to 2 kHz. Another study on consumer ANC headphone performance [5] suggests that the noise reduction achievable by ANC headphones is typically between 10 − 25 dB, and the performance is highly dependant on the tightness of the wearing situation.
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Simple feedback structure of active noise control in a duct

Simple feedback structure of active noise control in a duct

T oday, tw o basic m ethods are used for active noise control; feed-back and feed-forward. They are using all capabilities o f contem porary com puters and other electronics. These systems are used m ainly in conjunction w ith adaptive filters, w hich m ake them capable to cope w ith bad system response. B ut there is a question, if it is possible (in som e cases) to use a sim pler system , w hich is efficient enough and at the sam e tim e cheaper and m ore reliable because o f less electronic com ponents. This is the purpose o f the experim ental ventilation duct that w as m ade in this research.
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Filtered X Affine Projection Algorithms for Active Noise Control Using Volterra Filters

Filtered X Affine Projection Algorithms for Active Noise Control Using Volterra Filters

1997, during his Ph.D. studies, he spent several months as a Visiting Scholar at the University of Utah, Salt Lake City, USA. From 1997 to 2003, he worked as a DSP engineer with Telit Mobile Terminals SpA, Trieste, Italy, where he was leading the audio processing R&D activities. In 2003, he worked with Neonseven srl, Trieste, Italy, as audio and DSP expert. From 2001 to 2004, he collaborated with the University of Trieste as a Contract Professor of Digital Signal Processing. Since 2004, he is an Associate Professor at the Informa- tion Science and Technology Institute (ISTI), University of Urbino, Urbino, Italy. His research interests include adaptive filtering, non- linear filtering, nonlinear equalization, acoustic echo cancellation, and active noise control.
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A Method to Improve the Performance of Active Noise Control System Based on Time Delay Estimation

A Method to Improve the Performance of Active Noise Control System Based on Time Delay Estimation

With the development of economic level, people's requirements for environmental quality have gradually increased. Noise control has been widely studied in recent years. For noise equipment with a certain volume and complex channel, active noise control can be used in a certain space [1]. The target area is wrapped with a number of secondary sound sources, reference microphones, and error sensors [2]. The secondary sound source is used to output the acoustic wave with the same frequency and opposite phase as the original noise, and cancel the original noise to achieve the purpose of reducing the noise in the space [3]. The ANC system generally adopts an adaptive algorithm, and continuously optimizes the channel parameters under certain optimization criteria, so that the estimation of the channel is gradually close to real. In actual use, the ANC system can effectively reduce the
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Phasor Representation for Narrowband Active Noise Control Systems

Phasor Representation for Narrowband Active Noise Control Systems

The problems of acoustic noise have received much attention during the past several decades. Traditionally, acoustic noise control uses passive techniques such as enclosures, barriers, and silencers to attenuate the undesired noise [1, 2]. These passive techniques are highly valued for their high attenuation over a broad range of frequency. However, they are relatively large in volume, expensive at cost, and ineffective at low frequencies. It has been shown that the active noise control (ANC) system [3–14] can efficiently achieve a good performance for attenuating low- frequency noise as compared to passive methods. Based on the principle of superposition, ANC system can cancel the primary (undesired) noise by generating an antinoise of equal amplitude and opposite phase.
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Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

Nlfxlms and thf-nlfxlms algorithms for wiener-hammerstein nonlinear active noise control

Noise pollution has many negative effects on human health, such as: hearing loss, cardiovascular disease, mental illness and negative social behaviour [1] and [2]. Figure 1.1 shows that in most states of Malaysia the noise level exceed both day and night time limits. High frequency noise can be controlled using passive methods, for example barriers and silencers. However, this method is not effective for low frequency noise below 500Hz, because this low frequency noise has longer wavelength that allows the noise to penetrate through the barriers and silencers [3]. An active noise control (ANC) method is on effective method that can be used to cancel low frequency noise using the principle of superposition.
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An Efficient Filter Design for Active Noise Control System

An Efficient Filter Design for Active Noise Control System

Active noise control (ANC) is a method that is used to suppress audio noise. This system uses an array of secondary sources to produce an antinoise wave, which cancels the noise wave. The antinoise signal is a wave with same amplitude and opposite phase to that of the noise signal. ANC works on the principle of superposition. The anti noise signal will be made to superimpose with the noise signal, and effectively they will cancel and a noise free output will be obtained. The secondary sources will be connected by an appropriate signal processing algorithm. Many applications find use of ANC such as in industries, automobiles etc.
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Active Noise Control in a Luxury
Vehicle

Active Noise Control in a Luxury Vehicle

A significant step prior to application of a real-time controller on the vehicle in- volves the and evaluation of the controller’s performance at several accelerometer locations and directions. In this chapter we determine the fundamental adaptive algorithms for multichannel active noise control. We then, compare the reduc- tions we achieved at different reference accelerometer locations and directions. This approach can allow to understand how locations that are essential in NVH road noise analysis, such as TPA-based methods are related to the performance of a multichannel controller, which uses the same input accelerometer locations as TPA. This could potential be advantageous, in case specific locations of the vehicle are known for their significant contribution to structure-borne road noise from TPA or other road noise NVH methods as the installation of the controller can be faster and more robust in terms of the reference sensor location. In this simulation study we examine the performance of a multichannel controller based on the geometry of the sensitive structural parts, in order to reveal the relation between the directions that are used in TPA methods. The common ground of ARNC and TPA is that both aim to synthesise accurately road noise, but with different input signals. In particular, TPA methods use force signals as inputs, whereas ARNC acceleration signals. However, both methods require measure- ment locations at several points on the suspension or other axle parts that are usually causing or allowing low frequency vibrations. On that basis, we will in- vestigate most of the parts that are usually found in TPA analysis, in order to understand the physical relationship between the reference acceleration signals and the ARNC operation.
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Low frequency active noise control of an underwater large scale structure with distributed giant magnetostrictive actuators

Low frequency active noise control of an underwater large scale structure with distributed giant magnetostrictive actuators

Prior to the experiments the tank was sealed and water was injected to increase the hydrostatic pressure to 1 MPa. The active noise control experiments were carried out at the stable pressure value of 0.96 MPa. The active control process is shown in Fig. 10. There are three spectra in Fig.10. The first spectrum displays the time-domain results, the second spectrum shows the frequency-domain results and the third spectrum is the sound absorption coefficient of the active noise control. In the first and second spectrum, the red line is the primary signal from a signal generator (type: Agilent 33220A) and the white line is the secondary sound signal. The sound pressure level of the error signal reduced from 128 dB to 103 dB when the incident frequency was 1.3 kHz. The convergence time of the total active control was less than 1 s. The time domain and frequency domain of the signal can be found in Fig.10 and the current frequency is clearly displayed in the frequency domain.
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Methodology Active Noise Control System for Real Time Noise Reduction Using the Tms320c5416 Processor
Nahid Jabeen & Dr Sachin Saxena

Methodology Active Noise Control System for Real Time Noise Reduction Using the Tms320c5416 Processor Nahid Jabeen & Dr Sachin Saxena

The Active Noise Control [ANC] was installed as a programming the DSP unit TMS320C5416 and the outcomes were observed to be like the outcomes created by Matlab. The continuous use of the Active Noise channel was effective. Current DSP Kits like TMS320C5416, TMS320C6211 are sufficiently quick to give the rate and unwavering quality of constant Noise separating. The importance of this DSP Kit demonstrates the real headways in the field of sign handling which will dramatically affect Real-Time preparing is finished. Smaller gadgets implanted with TMS processors introduced can be utilized as successful correspondence types of gear.
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Online Active Noise Control System Design and Implementation

Online Active Noise Control System Design and Implementation

The primary objective of this project is to propose a new methodology for low cost and high performance Active Noise Control (ANC) system to enhance the constraint, here the online or real time Active Noise Control System constructs by using ARM CORTEX M3 controller with inbuilt high speed Analog to Digital (A/D) converter and Digital to Analog (D/A) converter.

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Multi objective genetic algorithm optimisation approach for the geometrical design of an active noise control systems

Multi objective genetic algorithm optimisation approach for the geometrical design of an active noise control systems

This paper focuses on the geometrical design of active noise control (ANC) in free- field propagation medium. The development and performance assessment uses genetic optimisation techniques to arrange system components so as to satisfy several performance requirements, such as physical extent of cancellation, controller design restriction and system stability. The ANC system design can be effectively addressed if it is considered as multi – objective optimisation problems. The multi-objective genetic algorithms (MOGAs) are well suited to the design of an ANC system and the approach used for it is based on a multi - objective method, with which the physical extent of cancellation and relative stability assessment are dealt with simultaneously.
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Performance Evaluation Of Active Noise Control Algorithm Using Matlab

Performance Evaluation Of Active Noise Control Algorithm Using Matlab

When the noise is predictable such as a narrowband noise or a tonal noise of some discrete frequencies this constraint is not seen and hence the noise need not be sensed from the source and it can be generated locally by a noise generation circuit or an algorithm. The schematic diagram of narrowband ANC algorithm is shown in Fig. 3. When the primary noise is periodic the input microphone can be replaced by a non-acoustic sensor like tachometer, accelerometer and optical sensor. This type of sensor eliminates the problem of acoustic feedback. The non-acoustic sensor signal is used to simulate an input signal that contains the fundamental frequency and all the harmonics of the primary noise.
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Analog Active Noise Control System

Analog Active Noise Control System

An individual’s noise exposure is a measure of the noise experienced by the individual over a period of time. A noise level is a measure of noise at a given instant in time. However, noise levels rarely persist consistently over a long period of time. Community noise varies continuously over time with respect to the contributing sound sources of the community noise environment. Community noise is primarily the product of many distant noise sources, which constitute a relatively stable background noise exposure, with individual contributors being unidentifiable. Background noise levels change throughout a typical day, but do so gradually, corresponding with the addition and subtraction of distant noise sources and atmospheric conditions. The addition of short duration single event noise sources (e.g., aircraft flyovers, motor vehicles, and sirens) makes community noise constantly variable throughout a day. [9]
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An experimental model to measure the ability of headphones with active noise control to reduce patient’s exposure to noise in an intensive care unit

An experimental model to measure the ability of headphones with active noise control to reduce patient’s exposure to noise in an intensive care unit

we are unable to rule this out. Additionally, the model design precluded trial of ear- plugs as a comparison group as the sound meter occupies the auditory meatus. There- fore, only very limited comparison between effectiveness of earplugs and headphones with ANC can be made. While every effort was made to ensure that the model was set up identically on each sampling day, it is possible that small differences in the model setup could result in artefactual differences in the noise levels recorded. We have con- tacted the manufacturers of the headphones used in this study, and they have unfortu- nately been unable to supply substantive claims of actual noise reduction levels in either laboratory or real-life environments, including clinical. Finally, while costs of headphones with ANC are falling and re-use of headphones between patients is pos- sible, it is likely that the cost of headphones with ANC will likely exceed that of single use earplugs for the foreseeable future.
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Active Noise Control Over Spatial Regions

Active Noise Control Over Spatial Regions

We formulated the spatial ANC problem using the wave domain signal process- ing technique in Chapter 4. For spatial ANC in the general noise field, we imple- mented the conventional filtered-x least mean square framework in wave domain, resulting in the wave-domain FxLMS algorithm. To the best of our knowledge, we are the first team to systematically formulate the wave-domain ANC into dif- ferent minimization problems and different updating variables in Chapter 5. The algorithms we proposed have been evaluated in free field and room environment through numerical simulations. Meanwhile, in the numerical simulations, we ex- ploited the spatial ANC performance using different numbers of secondary sources. In the scenario of fewer secondary source than the requirement, we demonstrated that normalized energy based wave-domain algorithms could achieve better noise reduction performance over the region. Meanwhile, the reduced noise reduction performance in the steady state demonstrated the limit of wave-domain ANC us- ing finite resources.
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