Noise Attenuation Research Articles

Modulation of wake evolution, separation, and radiated noise by a cylinder with porous media cladding

In this paper, the effects of porous media parameters on circular cylinder wake flow and radiation noise are investigated using large eddy simulations and Ffowcs Williams–Hawkings acoustic analogy. We performed three-dimensional numerical simulations for flow around the cylinder coated with a porous layer of different pores per inch in a subcritical flow regime (ReD=4.7×104) to explore the control mechanism of porous media on wake and radiation noise. The results show that the application of porous media significantly alters the separation pattern behind the cylinder and stabilizes the shear layer detached from the cylinder. The existence of porous layers leads to the transformation of chaotic and irregular vortex structures into more orderly vortices. Moreover, this study also reveals that the cylinder coated with high pore density can provide the desired noise reduction. The analysis of vortex sound theory indicates that porous media reduces the interaction area and magnitude of the positive and negative Lamb vector divergences, which is beneficial for drag reduction and noise attenuation. In addition, the comparison of sound pressure contours shows that the application of porous media does not change the radiation mode of noise, but the porous media with high pore density helps to decrease the generation of noise and intensity of the sound source.

Protocol-Based SMC for Interval Type-2 Fuzzy Semi-Markovian Jumping Systems With Channel Fading

The sliding mode control (SMC) problem is studied for interval type-2 fuzzy semi-Markovian jumping systems (S-MJSs) subject to channel fading. To reduce the network burden, a dynamic event-triggered protocol is adopted to improve the transmission efficiency. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to random noise and amplitude attenuation during the wireless communication. The main challenge lies in designing an appropriate fuzzy control scheme to achieve the reachability of the specified sliding region in line with channel fading. Under the common sliding surface, a fuzzy SMC law is constructed, which is related to the state signals affected by dynamic event-triggered protocol and channel fading. Then, by means of the boundary information of the global membership functions, sufficient conditions to ensure the stochastic stability of the underlying system, and the system states can be driven on the specified sliding region within a finite-time interval, which attenuates the influence of the channel fading. In the end, the tunnel diode circuit model is simulated to verify the proposed SMC strategy.

Unsupervised seismic random noise attenuation by a recursive deep image prior

The presence of random noise in field data significantly reduces the precision of subsequent seismic processing steps. As a result, random noise suppression is essential to improve the quality of field data. Because most traditional algorithms characterize seismic data linearly, the denoising accuracy is still open to be improved. As an unsupervised deep-learning method, the deep image prior (DIP) algorithm can characterize seismic data nonlinearly. The DIP uses randomly generated noise as input and noisy seismic data as desired output for random noise attenuation over several rounds of training epochs. However, determining the optimal training epoch for obtaining the final denoised result of unlabeled noisy data remains a challenge. To terminate the DIP training in time and obtain the denoised result, we design an improved quality control criterion (IQCC) based on adjacent estimations of seismic signal. To further improve the denoising accuracy, a recursive strategy is developed that uses the previous desired output as the new input and the previous denoised result as the new desired output. To obtain the optimal denoised results using the suggested recursive algorithm, a convergence condition also is established. Numerous examples of synthetic prestack and poststack data demonstrate the effectiveness of the designed IQCC and our recursive strategy with a convergence condition in protecting the effective signal, especially when compared with the curvelet thresholding algorithm and the original DIP. Furthermore, the denoising accuracy is on par with that of the supervised learning algorithm, demonstrating the adaptability of our recursive DIP under the convergence condition. Its superiority is further supported by field poststack seismic data processing, which uses the local similarity for performance assessments.

Quadrature Sinusoidal Signals Correction of Magnetic Encoders via Radial Basis Function Neural Network and Adaptive Loop Shaping

Magnetic encoders are widely used in industrial motion control, due to their low-cost, simple structures and low environmental requirements. However, the obtained quadrature sinusoidal signals suffer from various disturbances, which affects the accuracy of the magnetic encoders. The current methods which combine the neural network with phase-locked loop (PLL) typically require the knowledge of harmonic orders in advance and use a proportional-integral controller as loop filter of the PLL. In this paper, we propose a new method, in which a radial basis function neural network (RBFNN)-based PLL is combined with adaptive loop shaping. In this method, with the incorporation of RBFNN into PLL, the disturbances could be readily eliminated, thus avoiding additional parameter identification. Furthermore, the adaptive loop shaping served to redesign the PLL's loop filter, aiming to strengthen the high-frequency noise attenuation capability. The method has been validated both theoretically and experimentally, confirming that it is an effective method to improve the accuracy of the magnetic encoders.

H∞ filtering for discrete-time cyclic switched systems: Relaxed cycle-dependent persistent dwell-time constraints with averaging treatment

This note investigates the H∞ filtering for a kind of discrete-time switched systems ruled by cyclic switching regularities. By virtue of the cyclic manipulation of dwell-time (DT) switching and average dwell-time (ADT) switching reported in the literature, an innovative switching strategy of cycle-dependent persistent dwell-time (CPDT) property is first developed referring to the original persistent dwell-time (PDT) scheme. Further, to overcome the cycle-dependent DT constraint appearing in the CPDT switching, cycle-dependent ADT is sub-regionally injected into the suffering areas, which is different from the full-regional injection of ADT into the PDT switching in the literature. In this way, the CPDT switching is advanced to the cycle-dependent average persistent dwell-time switching, which can bring more flexibility to the switching design. Based on the newly introduced switching mechanisms, quasi-time-dependent (QTD) stability and l2-gain criteria are formulated for discrete-time cyclic switched systems to guide the design of intended QTD full-order filters, through which the produced filter error systems perform as expected in terms of the stability and H∞ noise attenuation capability. At last, the validities and advantages of the obtained filtering solutions are expounded by specific illustrative examples.

Design and demonstration of composite mufflers based on dissipative and reactive units

We have proposed and validated a design of a composite muffler. By a combination of dissipative and reactive units with different operating mechanisms, the advantages of both structures are utilized to achieve high transmission loss (TL) from low to high frequency in the ventilation duct system. A dissipative muffler composed of porous sound absorbing materials (PSAM) is chosen for noise attenuation above 1000 Hz, and a reactive muffler composed of Helmholtz resonators is used to reduce the noise below 1000 Hz. The combination of the two can achieve a TL above 20 dB in the broadband range of 244–1600 Hz and up to 30 dB in the vast majority of the frequency band, demonstrating excellent noise reduction effect. The proposed composite muffler has great advantages in broadband noise reduction for ventilation systems.

Preliminary study of reuse of plastic bottles as metamaterial

The present work introduces a novel approach to designing metamaterial for low frequency and high frequency wave attenuation by repurposing plastic waste. The metamaterial is constructed using a duct-like structure with a periodic arrangement of bottles of different sizes. This configuration leads to the formation of a local resonant band, which spans from 240 to 340 Hz. In both the numerical model and the experimental setup, a harmonic excitation is given to the proposed configuration and transmission loss is computed as a function of frequency. The experimental results support the findings obtained from numerical simulations, confirming the effectiveness of the designed metamaterial. One crucial aspect discovered in this work is that introducing space between the bottle’s neck and the side wall of the structure causes the bottles to act as Helmholtz resonators. Thus, in turn, this results in an additional band gap at low frequencies, complementing the existing Bragg’s band gap achieved by the periodic arrangement of bottles. The ability to change the size of the bottles gives control over the position of the band gap, allowing for greater flexibility in tailoring the metamaterial to specific noise attenuation requirements. The applications of these plastic bottle-based metamaterials are wide ranging, especially in improving the indoor acoustic environment of buildings. This work opens up a new avenue for further exploration and innovation in the field of sustainable and environmentally friendly metamaterials.

Super-tough, super-elastic, temperature-responsive, and tunable viscoelastic elastomer enabled by embedding nanosized liquid metal droplets

Liquid metal (LM) based composites are playing irreplaceable roles in many emerging fields such as stretchable and wearable electronics, soft robotics. However, it is still challenging to facilely fabricate the LM-based elastomers with nanosized and well-dispersed LM domains. Herein, the LM droplets filled elastomer nanocomposites (ENCs) with temperature-responsive, super-tough, super-elastic and tunable viscoelastic properties are introduced. By employing the conventional rubber processing method, LM is fragmented into nanoscale droplets and dispersed uniformly in cross-linked natural rubber (NR) without compromising the soft and highly stretchable properties of the matrix. In addition to the remarkable enhancement in tear resistance, the toughness of the resulting composites is strikingly improved as lowering the applied temperature, which is attributed to the phase transition and the simultaneous volume expansion of LM droplets. Surprisingly, for the viscoelasticity, this LM-based ENCs exhibit almost the same dynamic hysteresis with the pure NR system at the service condition of automobile tires, which is remarkably reduced compared to the traditional ENCs filled with rigid nanoparticles. Furthermore, this material also shows a good damping property for noise attenuation in the case of submarine covering. Collectively, this work opens a new avenue for the next generation of high-performance and multifunctional ENCs equipped in low-temperature working conditions.

Multistage residual network for intense distributed acoustic sensing background noise attenuation

Owing to its advantages in acquisition properties, distributed acoustic sensing (DAS) is gradually being applied in seismic exploration. Unfortunately, the acquired DAS records are usually contaminated by various unwanted interferences, which are considered one of the main obstacles for subsequent processing, such as inversion and imaging of the seismic data. In general, conventional signal processing methods cannot satisfy the requirements of DAS noise attenuation owing to the limitations in presumption and denoising accuracy. Therefore, deep learning, especially convolutional neural networks (CNNs), has been used to eliminate intense background noise and improve the quality of DAS records. Nevertheless, most CNN-based methods often rely on single-scale features and cannot guarantee denoising performance when dealing with complex DAS data. In this study, we develop a multistage residual network (MSR-Net) aiming to enhance denoising ability for complex DAS background noise. More precisely, the backbone of MSR-Net adopts a multiscale architecture to capture informative features within the DAS data. A modified ringed residual module is also used to enhance the representation ability of potential features. In addition, a feature aggregation spatial attention module is designed to refine and reinforce the primary features, thereby positively impacting the denoising performance. Meanwhile, an authentic training data set is generated based on field noise data and synthetic records obtained by the forward-modeling method. Compared with conventional methods and typical denoising networks, MSR-Net demonstrates superiority in weak signal recovery and intense DAS noise attenuation.

Modified non-local means: A novel denoising approach to process gravity field data

Abstract Gravity measurement is a basic geophysical method for non-destructive exploration of mineral resources and hydrocarbons and also for geological studies. The quality of gravity data measured is constantly degraded by a low signal-to-noise ratio. Noise attenuation thus plays a vital role in processing and interpreting gravity field data. The non-local means (NLM) filtering was first and successfully introduced to attenuate randomly distributed noise situated in seismic records in geophysical community. However, less attention has been drawn to apply NLM to denoise potential field data, since the success of NLM is guaranteed by carefully tuned parameters and large computational costs. Here we propose the modified NLM (MNLM), based on unweighted Euclidean distance and integral image method, to denoise gravity datasets. The unweighted Euclidean distance is used to reduce the uncertainty and complexity of tuning the control parameters involved, and the integral image strategy is applied to avoid the enormous computational cost of the NLM. Since these filtering properties are desirable to mitigate noise in gravity datasets, we test and evaluate the MNLM filter on noisy synthetic gravity models with uniform and normal distributions and on real data from the Mariana trench and Slovakia, and compare it to other standard and representative denoising filters. The results on synthetic and field datasets confirm the high speed of this modified algorithm and show that, it removes noise most effectively while clearly preserving and not blurring structural details with less tuned parameters. The MNLM filter can therefore be considered as a promising and novel algorithm for denoising gravity data.

Complete perception self-attention network for weak seismic signal recovery in distributed acoustic sensing vertical seismic profile data

Distributed acoustic sensing (DAS) is a new technology for acquiring seismic data with high spatial resolution at low cost. Furthermore, in real downhole seismic exploration, DAS can receive some weak signals reflected from deep and thin layers. Unfortunately, some real downhole seismic data received by DAS often are characterized by low quality. Specifically, in real DAS records, desired signals with weak energy often are contaminated by some new noise not presented in seismic data received by conventional electronic geophones. Due to the characteristics of seismic data, such as frequency band aliasing, low signal-to-noise ratio, and complex noise wavefield, existing linear or nonlinear denoising methods based on information processing theories cannot effectively eliminate this complex and multitype noise. Recently, the deep-learning method has been regarded as a powerful tool for background noise attenuation in seismic data. Most of the existing deep-learning methods are concerned with local features and ignore the global features that can be used to enhance their performance further. To simultaneously extract global and local features, we design a novel complete perception self-attention network (CP-SANet) based on the transformer framework and apply it to the denoising of downhole DAS records. The network embeds a transformer module into multilevel encoder-decoder framework. Depth-wise convolution is applied to enhance the local perception capability. Given the transformer’s requirement for a large amount of data, we specifically design abundant seismic data samples using formation models with different parameters. The noise in the data sets is obtained from actual field DAS data. The effectiveness and feasibility of CP-SANet are verified on synthetic and field DAS records. All of the experimental results prove its satisfactory performance compared with some classical and network methods.

Analysis of vortex structures evolution and aeroacoustic characteristics in the ultra-high-speed elevator ring-gap flow field

The continuous improvement of elevator speed has made the issue of aerodynamic noise in the hoistway more prominent. Previous research has usually focused on the characteristics of aerodynamic loads and related safety issues, and little attention has been paid to the problem of flow-induced noise. This paper established a three-dimensional geometric configuration of the ultra-high-speed elevator to study the flow behavior and aerodynamic acoustic characteristics in the hoistway using well-validated large eddy simulations. Firstly, we analyzed the unsteady flow behavior in the ring-gap flow field using large eddy simulations and captured the transient vortex structure in the flow field using the Q-criterion. We then predicted the far-field aerodynamic noise of the elevator car using the Lighthill-Curle aerodynamic acoustic equations. The results showed that the factors affecting the sound source intensity of the elevator car include the shedding position and intensity of the vortex structures. By adjusting the shedding position and reducing the intensity of the vortex structure, the sound source intensity of the elevator car wall could be effectively controlled. The change of the blocking ratio could not affect the attenuation of aerodynamic noise in the hoistway, but the increase of the blocking ratio could lead to an increase in the turbulent kinetic energy intensity and peak SPL in the hoistway. Therefore, the blocking ratio should be kept within 0.65 when designing the hoistway structure dimensions.

Model-inversion control to enforce tunable Duffing-like acoustical response on an Electroacoustic resonator at low excitation levels

The electroacoustic resonator is an efficient electro-active device for noise attenuation in enclosed cavities or acoustic waveguides. It is made of a loudspeaker (the actuator) and one or more microphones (the sensors). So far, the desired acoustic behaviour, expressed in terms of a linear-time-invariant relationship between sound pressure and vibrational motion (the acoustical impedance), has been more efficiently achieved by a model-inversion strategy which is implemented by driving the electrical current in the loudspeaker coil, based upon the measured pressure. The corrector transfer function is defined in the Laplace domain and digitally executed by the classical infinite-impulse-response technique, though a state–space representation could be employed. In this work, we are interested in enforcing a nonlinear behaviour at low sound excitation levels, where the electroacoustic resonator would normally behave as a linear-time-invariant system. Hence, in order to transform its acoustical response from linear to nonlinear, the model-inversion technique must be reformulated in time domain. The state–space representation of the relationship between the input measured pressure and the output electrical current gives the right perspective and the solution to this problem. We provide the conception of this model-inversion control algorithm capable of transforming a linear-time-invariant acoustical response to potentially any causal acoustical response of the electroacoustic resonator. Such control strategy is tested by targeting a Duffing acoustical response with tunable parameters. Both numerical simulations and experimental tests in quasi-open field validate the approach. The results provided in this contribution open the doors for conceiving non-conventional absorbers which can exploit nonlinear phenomena for noise mitigation even at low excitation amplitudes.

Active attenuation of free field noise in a spatial domain using neural networks inferred controller

In this talk, we explore the application of deep neural networks (DNNs) to infer the decoupling of an incoming acoustic array, distinguishing between the signal of interest and noise components. The objective is to leverage this decoupling information to develop an effective controller that can attenuate the noise, thereby improving the quality of the desired signal. The decoupled noise is then fed into a specially designed controller to attenuate the noise while preserving the desired signal. The controller leverages the DNN's predictions to adaptively adjust its parameters, allowing it to adapt to changing noise conditions and improve noise reduction performance. The approach is evaluated using simulations and real-world acoustic array measurements. The results demonstrate the performance of the DNN-based decoupling and noise attenuation system in reducing noise while preserving the quality of the desired signal. The proposed system offers effective adaptive solutions for enhancing the quality of desired signals in the presence of noise, with potential applications in areas such as speech recognition, audio communication systems, and environmental noise suppression.

Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices.

This tutorial intends to provide insight, instructions and "best practices" for those who are novices-including clinicians, engineers and non-engineers-in extracting electromyogram (EMG) amplitude from the bipolar surface EMG (sEMG) signal of voluntary contractions. A brief discussion of sEMG amplitude extraction from high density sEMG (HDsEMG) arrays and feature extraction from electrically elicited contractions is also provided. This tutorial attempts to present its main concepts in a straightforward manner that is accessible to novices in the field not possessing a wide range of technical background (if any) in this area. Surface EMG amplitude, also referred to as the sEMG envelope [often implemented as root mean square (RMS) sEMG or average rectified value (ARV) sEMG], quantifies the voltage variation of the sEMG signal and is grossly related to the overall neural excitation of the muscle and to peripheral parameters. The tutorial briefly reviews the physiological origin of the voluntary sEMG signal and sEMG recording, including electrode configurations, sEMG signal transduction, electronic conditioning and conversion by an analog-to-digital converter. These topics have been covered in greater detail in prior tutorials in this series. In depth descriptions of state-of-the-art methods for computing sEMG amplitude are then provided, including guidance on signal pre-conditioning, absolute value vs. square-law detection, selection of appropriate sEMG amplitude smoothing filters and attenuation of measurement noise. The tutorial provides a detailed list of best practices for sEMG amplitude estimation.

Method to measure the effect of acoustic leaks on active noise reduction device performance

Passive hearing protection (HP) systems are designed to reduce the level of noise at the ear. Passive HP typically excel in attenuating mid- to high-frequency noise. Active noise reduction (ANR) has the potential to provide additional attenuation of low-frequency noise. There are documented reports describing unwanted sound being generated by ANR systems if an acoustic leak is present. The objective of this effort was to develop a method to measure the performance of ANR devices with and without the introduction of an acoustic leak. A number of ANR earcups were selected for this study. Data were collected with an artificial ear and a flat base plate in a noise environment that ranged from 65–115 dB SPL. Wedges, designed in-house, created a consistent acoustic leak that ranged in size from 1–2 mm in radius. The results revealed that some ANR earcups provided high levels of protection when the earcup was well-sealed; however, when an acoustic leak was introduced, the earcups became unstable and generated noise in the earcup. This instability was not seen in all ANR devices. The outcome of these measurements solidified the need for a method to measure ANR device performance when acoustic leaks are introduced.

Event-Triggered SMC for Networked Markov Jumping Systems With Channel Fading and Applications: Genetic Algorithm.

The event-triggered sliding-mode control (SMC) for discrete-time networked Markov jumping systems (MJSs) with channel fading is investigated by means of a genetic algorithm. In order to reduce resource consumption in the transmission process, an event-triggered protocol is adopted for networked MJSs. A key feature is that the signal transmission is inevitably affected by fading phenomenon due to delay, random noise, and amplitude attenuation in a networked environment. With the aid of a common sliding surface, an event-triggered SMC law is designed by adjusting the system network mode. Under the framework of stochastic Lyapunov stability, sufficient conditions are constructed to ensure the mean-square stability of the closed-loop networked MJSs, and the sliding region is reached around the specified sliding surface. Moreover, based on the iteration optimizing accessibility of objective function, an effective SMC approach under genetic algorithm is proposed to minimize the convergence region around the sliding surface. Finally, the effectiveness of the proposed method is proved by the F-404 aircraft model.

Experimental study on the sound transmission loss suite at the University of Technology Sydney

Sound transmission loss suites are essential testing facilities for measuring the sound insulation properties of building elements and assessing noise attenuation. However, inconsistencies in test results can arise due to variations in the size, shape, and construction of test rooms across different laboratories, with biases introduced by the room acoustics or acoustical environment of the facility itself. To evaluate the reliability of such testing, we conducted an experimental study on the sound transmission loss suite at the University of Technology Sydney. Our investigation focused on three key factors: estimating the maximum achievable sound reduction using a heavyweight wall installed at the test aperture on the source room side, testing the effectiveness of vibration isolation between reverberation rooms, and assessing the decoupling of the sound field within the reverberation rooms.

The relationship between freight and passenger train noise levels and distance on the Sydney Metropolitan Rail Network across 281 measurement sites

Transport for NSW undertakes noise monitoring at a range of distances and locations around the NSW Government-managed rail network. The monitoring quantifies noise levels from freight and passenger train operations and informs delivery of noise reduction programs including the Transport for NSW’s Freight Noise Attenuation Program, which was launched in 2015. Comprehensive noise monitoring over a typical period of two weeks has been undertaken at 281 representative locations adjacent the passenger and freight rail network around Sydney. Results were analyzed to determine the LAE noise levels from each freight and passenger train passby, and the average weekday LAeq(15hour) daytime and LAeq(9hour) night-time levels. Curves of best fit are presented that illustrate the relationship between freight and passenger train noise levels and the distance to the near track. These curves of best fit can be utilised by Government agencies and acoustic practitioners to quantify the potential noise impact of freight and passenger train operations at a range of distances and locations close to the NSW Government-managed rail network and identify appropriate noise mitigation measures.

Coal mining noise in Australia

Noise pollution in open-cut coal mines in NSW is of great concern and affects large numbers of local communities. The management of noise from coal mines has evolved considerably over the last 30 years, in part due to the significant improvement in mobile fleet sound power levels due to new technology and leading practice noise attenuation components. The mining industry in Australia has also developed an innovative way of managing environmental noise driven by the use of proactive noise management systems. Most large open-cut mining operators in NSW have in recent years adopted and successfully implemented proactive noise management systems to assist with managing noise levels during periods of noise-enhancing meteorological conditions. The system allows the mine operator to prepare and adjust operations to reduce noise levels as far as reasonably and feasibly practical in the event anticipated adverse meteorological conditions are experienced. This paper describes the evolution of the regulatory process, the industry response and the key factors that have shaped the changes.