Active noise control Research Papers (original) (raw)

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- by
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- Signal Processing, Cross Correlation, Active noise control, Convergence Rate
- by Hocine Bougdah
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- Active noise control

AbstractThis paper proposes two modifications of the filtered-x least mean squares (FxLMS) algorithm with improved con-vergence behavior albeit at the same computational cost of 2M operations per time step as the original FxLMS update.... more

- by Markus Rupp
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- Engineering, Active noise control, Least Mean Square

The requirements set by the most important Air Traffic Management (ATM) research initiatives (SESAR and NextGen) and environmentally sustainable aviation research initiatives (Clean Sky, and Environmentally Responsible Aviation), aim at lowering operating costs and improving the environmental sustainability of aviation. In line with these requirements, the methodology of using noise modelling and simulation techniques in conjunction with Demographic Distribution Database (D3) and Digital Terrain Elevation Database (DTED), allows the development of novel approaches and algorithms for aircraft trajectory optimisation in the present and likely future airspace scenarios and, in particular, in proximity of airports and densely populated areas. Aircraft noise modelling in major Australian airports is essential in order to understand the noise impacts associated with air traffic, determining the Australian Noise Exposure Forecasts (ANEFs) and to assist in effective airport planning strategies. Based on the statuary requirements prescribed, the aircraft trajectories are optimised to minimize the effects of noise around the airports considering both population and digital terrain data. The key mathematical models for noise and trajectory optimisation are presented. The objective of the noise modelling process is to describe the noise parameters currently adopted for airport design and operations, and to predict values for all relevant future scenarios. Noise analysis and impact mitigation measures are discussed. Quantification of noise at the major Australian airports, including Sydney and Melbourne airports is presented.

- by Ralf Schlüter
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- Neural Network, Databases, Low Frequency, Recurrent Neural Network
- by Hocine Bougdah
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- Active noise control

Noise is consistently presents in digital images during image acquisition, coding, transmission, and processing steps. The main purpose of de-noising the image is to restore the detail of original image as much as possible. Depending on the types of disturbance, the noise can affect the image to different extent. Salt and Pepper noise arises in the image because of sharp and sudden changes of image signal. Dust particles in the image acquisition source or over heated faulty components can cause this type of noise. In the field of image processing, several type of linear and non-linear filtering techniques have been proposed for reducing the noise in images. Filters like Median, Gaussian and Wiener are used to do this comparative study of different images of same type in Gray scale. The quality of the enhanced images is measured by the statistical quantity measures: Peak Signal-to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM) metrics. An application specific measure for natural flower images correlation is defined and known as Deviation Measure. A Deviation Measure (DM) is obtained by the comparison of filtered and original image which shows the deviation in the images.

- by Shengting Li
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- Engineering, Signal Processing, High Frequency, Active noise control

1.1. Wavelength Calculation: λ = c / f Where: c = Speed of sound [m/s] f = Frequency of the sound [Hz] λ = Wavelength [m] 1.2. Sound pressure and sound power Sound pressure Lp = 10 log 10 (p/p 0) 2 re 20 * 10-6 [dB] p = Pressure of incoming sound wave in [Pa] p0 = reference pressure corresponding to 20*10-6 [Pa] Sound power Lw = 10 log10 (p/p0) re 10-12 [dB] P = Sound power of source in [w]. P0 = Reference sound power corresponding to 10-6 [w] 1.5 Graph used to predict attenuation values A' = 12.6 (⍶ sab) 1.4 * (P/39,37*S) Where: A' = Attenuation [dB/m] ⍶ sab = Sound absorption for the duct lining [-] P = Acoustically lined parameter of duct [m] S = Cross-sectional open area of duct [m2] Attenuation = A[(1+y|M|) / (1+M)] Where: A = Attenuation without flow [dB] M = Mach number y = Experimental sector 18

- by Nguyen Moc Duc
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- Mechanical Engineering, Active noise control

The focus of the paper is on evaluating active noise control (ANC) systems for effective noise reduction of the outdoor unit of air-conditioning system. The first problem intended to be solved is that the noise created by the outdoor unit will lead to noisepollution to the environment. The second problem is that the noise emitting from the compressor of airconditioning system varied with respect to time. The third problem is that the chosen ANC system must be able to cope with low and high frequency of the noise signal. The developed system to deal with aforementioned problems is the PC-based ANC system with normalised least-mean-square (NLMS) algorithm. Based on the testing result obtained, the average noise-reduction in actual outdoor unit is 0.9 dBA. The obtained result is encouraging and will form the foundation for future work on the same area.

- by SDIWC Organization
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- Active noise control, Noise reduction, Air Conditioning Systems, Outdoor Units

Noise emitted by heating, ventilation and air conditioning (HVAC) systems is one of the main causes of disturbance inside buildings. High frequency noise can be reduced using passive devices, but noise components at frequencies below 400-500 Hz are hardly reduced in such way. Active noise control (ANC) has its maximum efficiency at low frequency and then it is the ideal

- by Massimo Garai and +1
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- Low Frequency, High Frequency, Active noise control, Air Conditioning

In this paper, an active noise control (ANC) system is developed to provide an effective and non-intrusive solution for reducing loud snoring to provide a quiet environment for a snorer's bed partner. An adaptive least mean square (LMS) algorithm optimized for different kinds of snore signals is introduced and theoretically analyzed. Also, a residual noise masking approach is proposed to further reduce the effect of the snore noise without interfering with the LMS algorithm. Computer simulations followed by real-time experiments are conducted to demonstrate the feasibility of the snore ANC systems based on a pillow setup. For the optimum effect based on the characteristics of human hearing, the performance of the proposed approach is evaluated by using the multi-channel feedforward ANC systems based on the filtered-X least mean square (FXLMS) algorithm. Compared with a traditional headboard setup for snoring noise control, the proposed snore ANC systems optimized for ear field operation yield much higher noise reduction around the ears of the snorer's bed partner.

- by Luigi Piroddi
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- Mechanical Engineering, Applied Mathematics, Computer Architecture, FPGA

In this paper an active noise control (ANC) algorithm is proposed. This algorithm is based on adaptive filtering with averaging (AFA) and uses a similar structure as that of the FxLMS ANC system. The proposed algorithm, which we call FxAFA algorithm, uses averages of both data and correction terms to find the updated values of the tap weights of the

- by G.t.-c. Chiu
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- Mechanical Engineering, Robust control, Actuators, Active noise control
- by Prof. Dr. Mızrap Bulunuz
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- Noise, Active noise control, Noise reduction
- by Roy Leitch
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- Digital Signal Processing, Control system, Active noise control, Noise Control
- by Jean-marc Odobez
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- Engineering, Social Psychology, Signal Processing, Speaker Recognition

En este trabajo se propone una recombinación en árboles binomiales multiplicativa generalizada para la ecuación autónoma, en términos de la condición inicial y del producto entre saltos no constantes hacia arriba y hacia abajo del proceso discretizado. Se presenta de manera formal una técnica para encontrar las probabilidades de transición dinámicas considerando los dos primeros momentos del proceso solución de la ecuación diferencial, los cuales incorporan el factor de crecimiento y la volatilidad en términos de los parámetros y del proceso subyacente a lo largo de su ramificación. Se muestran algunos resultados numéricos experimentales de valoración de opciones Europeas para el proceso log–normal y para los procesos de reversión a la media con ruido aditivo y ruido proporcional para diferentes fechas de expiración.

- by Revista Ingeniería y Ciencia
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- Engineering, Mathematics, Applied Mathematics, Probability Theory

This paper considers an implementation of Least
Mean Square (LMS) adaptive filter for noise cancellation. In this
paper, the adaptive filter is used to create a “Zone of Silence”.
The paper considers the use of LMS adaptive filter to cancel
engine noise in a target area by generating a signal that will
interfere destructively with the noise signal, hence cancels the
noise introduced in the target area. The proposed model is
simulated using LabView software. The resuts show that the
proposed model can cancel out noise average power up to 7900
times.

- by Muhammad AB
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- Process Control, Circuits and Systems, Convergence, Computer Simulation

In this paper, a feed-forward nonlinear active noise control algorithm is developed using the Filtered-S LMS (FSLMS) algorithm and virtual microphone control for an active headrest application. Virtual microphone control is implemented to attenuate noise at the ears of the headrest occupant using the physical microphones placed at remote locations. The proposed nonlinear virtual microphone control algorithm is verified in real-time in a headrest. Different nonlinear noise processes are studied and it is shown that in all cases, the FSLMS algorithm outperforms the FXLMS algorithm in controlling nonlinear primary noise at a virtual location.► The virtual microphone control algorithm is combined with nonlinear ANC algorithm. ► Paper on multi-channel nonlinear active noise control. ► Real-time results for an active headrest are shown to control nonlinear noise. ► Active control of the transformer noise as a nonlinear noise control is newly proposed.

Developmental disorders such as autism have generally been theorized as due to some kind of modular “deficit” or “dysfunction”—typically of cortical origin, i.e., failures of “theory of mind”, of the “mirror neuron system”, of “weak central coherence” or of the balance of “empathizing” and “systemizing”, just to list a few. The broad array of autonomic and sensorimotor differences experienced and reported by people with autism have by such theories typically been sidelined as “co-morbidities,” possibly sharing genetic causes, but rendered as incidental and decisively behaviorally irrelevant symptoms—surely disconnected from cognition.
This article entertains the idea that the development of cortically based mental processes and autonomous control relies on the complexities and proper function of the peripheral nervous systems. Through such an “embodied” lens the heterogeneous symptoms of autism invites new interpretations. We propose here that many behavioral-level findings can be re-defined as downstream effects of how developing nervous systems attempt to cope and adapt to the challenges of having various noisy, unpredictable, and unreliable peripheral inputs.

- by Maria Brincker and +1
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- Neuroscience, Psychology, Cognitive Psychology, Cognitive Science
- by Paolo Peretti and +1
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- Applied Mathematics, Adaptive Control, Active noise control, Air Conditioning

Active Noise Control is a vast field in scientific research. The optimization of the open or closed loop of a feedback system is well explored and several optimization approaches are found in literature. It is common to measure the secondary path, i.e. the transfer functions of the microphone and the loudspeaker in an ANC-headphone system, via sweeps, which can be used as a reference model for the optimization of a feedback system. Although the system is optimized, the misuse of the headphone, i.e. a leak between the ear and the headphone cushion or pressing the ear cup against the head, can lead to instability. For an effective reference model several measurements have to be made. Especially the worst-case scenarios have to be taken into account. It is possible to determine different secondary paths for different measurement positions on the listener’s head. The combination of worst-case secondary paths, which are transfer functions of leaky and pressed down measurements, can be used as a set of worst case constraints for closed or open loop optimization. Within this contribution a method of defining worst-case constraints for the open and closed loop optimization of a regular secondary path to maximize the ANC-performance and never violate the stability constraints is presented.

This paper presents passive and active vibro-acoustic noise control methods for attenuating the interior noise level in box structures which can be an analogy of cabins of vehicle and aircraft. The structural intensity (SI) approach is adopted to identify the predominant vibration panels and interior noise sources for box structures. In the study, the finite element method is used to determine the structural vibration and structural intensity in the box surfaces. According to structural intensity vectors plot and structural intensity stream lines presentation, the possible effective control positions where the dampers may be attached and the active control forces may act to reduce vibration and interior noise, are identified. From the study, it can be demonstrated that the structural intensity approach and stream line presentation are possible methods for identifying the vibro-acoustic interior noise source and predominant panels which may be modified to reduce the interior noise level. The structural intensity methodology, passive and active noise control results can be extended to the further study of the vibration and interior noise control of actual cabins of vehicles and aircraft.

- by Zishun LIU
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- Acoustics, Aeroacoustics, Active noise control, Virtual sensing

The paper constitutes an application of Active Noise Control (ANC) on noise from wind turbine blades and rotors. This study aims at considering the potential of using ANC on wind turbines. To this end, a review was carried out on infrasound and low frequency noise emanating from wind turbines. The basis of human interaction with low frequency noise from wind turbines and its effects are studied, their effects on the
environment, past approaches used for their reduction and the potentiality of using ANC. The performance of the system was evaluated by carrying out simulations. The results show that ANC systems are capable of attenuating infrasound and low frequency noise from wind turbines.

- by Gloria G A Adewumi and +2
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- Wind Energy, Carbon Nanotubes, Nanotechnology, Active noise control
- by Mahesh Chandra
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- Active noise control, Thresholding, Adaptive Filter, Least Mean Square

The current assessment of behaviors in the inventories to diagnose autism spectrum disorders (ASD) focus on observation and discrete categorizations. Behaviors require movements, yet measurements of physical movements are seldom included. Their inclusion however, could provide an objective characterization of behavior to help unveil interactions between the peripheral and the central nervous systems. Such interactions are critical for the development and maintenance of spontaneous autonomy, self-regulation and voluntary control. At present, current approaches cannot deal with the heterogeneous, dynamic and stochastic nature of development. Accordingly, they leave no avenues for real-time or longitudinal assessments of change in a coping system continuously adapting and developing compensatory mechanisms. We offer a new unifying statistical framework to reveal re-afferent kinesthetic features of the individual with ASD. The new methodology is based on the non-stationary stochastic patterns of minute fluctuations (micro-movements) inherent to our natural actions. Such patterns of behavioral variability provide re-entrant sensory feedback contributing to the autonomous regulation and coordination of the motor output. From an early age, this feedback supports centrally driven volitional control and fluid, flexible transitions between intentional and spontaneous behaviors. We show that in ASD there is a disruption in the maturation of this form of proprioception. Despite this disturbance, each individual has unique adaptive compensatory capabilities that we can unveil and exploit to evoke faster and more accurate decisions. Measuring the kinesthetic re-afference in tandem with stimuli variations we can detect changes in their micro-movements indicative of a more predictive and reliable kinesthetic percept. Our methods address the heterogeneity of ASD with a personalized approach grounded in the inherent sensory-motor abilities that the individual has already developed.

- by Maria Brincker and +2
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- Neuroscience, Developmental Psychology, Psychiatry, Statistics
- by Kim Stelson
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- Multidisciplinary, Control system, Active noise control, Real Time Control

I filed and served the above document today on landlord's attorney, one Jeff S. Bennett, Esq. who served the subject Motion to Dismiss (MTD) on me yesterday afternoon by email. The more he lags, the faster I get. In it you will find a summary of current Oregon law addressing common landlord-tenant issues as they apply to MOBILE HOME OWNERS on someone else's land, in this case the land is owned by a corporation and that corporation employs various minions each of whom does some compartmentalized task. All is well until there is a breach, then it becomes and adventure "through the looking glass" to get any human being to communicate in a meaningful way with this legally deaf tenant. The dialogue between is colorful and informative as this is not a typical eviction suit, in fact there is no eviction or defect to be corrected at this point, I only seek to prevent future harm. Defendant seeks attorneys' fees, of course!

- by abbyjo ovitsky
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- Privacy, Housing, Equality and Diversity, Disabled people movement

Life is full of sounds and we want to hear the pleasant and vital ones; while shunning the unpleasant and dangerous variety. All told we are becoming steadily more sound conscious, as the relatively enormous growth of the telephone, radio, phonographic recording and talking motion picture industries sufficiently attests. Sounds touch people in different ways. Sound is extremely important to feel the taste of life. It is what we experience through the senses that make life meaningful. Sound is important for communication, signaling system, for finding depth of objects (SONAR) etc. Sound has been used to study earth’s history, to explore for oil and gas, to study undersea earthquakes and volcanic eruptions, to research wind energy, to measure temperature in ocean, to measure global climatic change. Even during the Cold War, the U.S. Navy allowed a small number of oceanographers to make use of the SOSUS (SOund SUrveillance System) for research in tracking soviet submarines at long ranges.

- by Umer Farooq
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- Acoustic Engineering, Acoustics, Vibrations, Sound
- by Meelan Choudhari
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- Acoustics, Aeroacoustics, Active noise control, Virtual sensing
- by Stefan Hurlebaus
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- Mechanical Engineering, Civil Engineering, Signal Processing, Vibration Control
- by Hoai Nghia Duong
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- Fuzzy Control, Active noise control, Feedback, Fuzzy Neural Network
- by Ben Cazzolato
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- Algorithms, Acoustics, Nonlinear dynamics, Multidisciplinary
- by Gaurav Chauhan
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- Active noise control
- by Bhavin Gajjar
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- Low Frequency, Performance Evaluation, Computer Simulation, Active noise control
- by NAJIBAH AB LATIF
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- Active noise control
- by Rajesh Rajamani
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- Mechanical Engineering, Mechatronics, Control system, Active noise control

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

- by Masayuki Kawamata
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- Computer Simulation, Active noise control, Convergence Rate, Adaptive Filter

This paper considers Least Mean Square (LMS) algorithm to subsequently modify adaptive filter wieghts. The corresponding filter is used to cancel noise caused by powerline interference contained in Electrocardiograph (ECG) signal. The design is then implemented on LabView software. The simulation shows that the design can reduce powerline interference. Average powerline noise power contained in the signal is attenuated by more than 20 times.