Wide Variety Of Noises Research Articles

Assessing the impact of engine and non-engine urban noises on the calls of urban frogs: a natural experiment

Urban noise can potentially disturb the acoustic signals of animals inhabiting urban areas. Although a wide variety of noises from different sources is common in cities, the impact of non-engine noise on animal calls is less studied. In a natural experiment, we evaluated the effect of different urban noises on the advertisement calls of urban frogs (Eleutherodactylus nitidus) in the metropolitan area of Puebla, Mexico. We recorded and analyzed 672 advertisement calls from 28 male frogs and the corresponding environmental noises from three distinct localities. Our analysis focused on four acoustic properties of the calls: inter-call interval, call duration, dominant frequency, and call amplitude. To standardize the amplitude measurements of sound pressure levels produced by noise and frog calls, we employed a practical approach using a reference signal for calibration. We treated the longitudinal data of different noises occurring before, during, and after advertisement calls as repeated measures within the urban locations. During our samplings, frogs called amidst spontaneous urban noises, including dog barking, fireworks, and vehicle engine sounds. Our results indicate that vehicle engine sounds and dog barking do not cause significant distortions in the calls of these urban frogs. However, we observed slight variations in the dominant frequency of calls, decreasing by 11 Hz, during and after fireworks. Given the observed plasticity of E. nitidus in response to noise, urban noises may not pose a severe problem for this urban frog.

An intelligent wireless channel corrupted image-denoising framework using symmetric convolution-based heuristic assisted residual attention network

ABSTRACT Image denoising is one of the significant approaches for extracting valuable information in the required images without any errors. During the process of image transmission in the wireless medium, a wide variety of noise is presented to affect the image quality. For efficient analysis, an effective denoising approach is needed to enhance the quality of the images. The main scope of this research paper is to correct errors and remove the effects of channel degradation. A corrupted image denoising approach is developed in wireless channels to eliminate the bugs. The required images are gathered from wireless channels at the receiver end. Initially, the collected images are decomposed into several regions using Adaptive Lifting Wavelet Transform (ALWT) and then the “Symmetric Convolution-based Residual Attention Network (SC-RAN)” is employed, where the residual images are obtained by separating the clean image from the noisy images. The parameters present are optimized using Hybrid Energy Golden Tortoise Beetle Optimizer (HEGTBO) to maximize efficiency. The image denoising is performed over the obtained residual images and noisy images to get the final denoised images. The numerical findings of the developed model attain 31.69% regarding PSNR metrics. Thus, the analysis of the developed model shows significant improvement.

Learning to extract buildings from ultra-high-resolution drone images and noisy labels

ABSTRACT Building maps have a plethora of applications in government, industry and academia. In most cases, large scale maps can be retrieved from OpenStreetMap vector data. However, for certain rapidly changing built and semi-built environments, corresponding maps are not as accurate and contain label noise such as missing, incorrectly present, shifted labels, etc.; mainly because buildings in those regions are constantly being constructed, deconstructed, replaced and altered. One such case is extant in the Rohingya camps of southeastern border region of Bangladesh. Mass refugee influx in late 2017 and following population growth has necessitated the construction of buildings and expansion of camps. Consequently, reliable methods are necessary for detecting and documenting camp buildings. Ultra-high-resolution drone images of Rohingya camps are semantically segmented through fully convolutional U-Net deep learning systems for generating accurate building maps from noisy labels. A wide variety of noises are prevalent in the labels. Deep learning systems provide less noisy predictions compared to the classification tool in the most widely used Geographic Information System (GIS) software, ArcGIS. Data augmentation and regularization allows reliable learning, even in the presence of label noise. During testing, calculation of numeric performance metrics against noisy labels can grossly underestimate true skill and performance of the model. A subset of 22 million pixels of the testing data is relabelled by hand to obtain noise-free labels. Testing our generated maps against noisy and noise-free labels confirms that true performance is higher than otherwise indicated by freely available building maps. Empirical results reveal that utilized pipeline is able to learn from noisy data and produce labels which are more accurate and less noisy. Labels generated by our best performing system provide Intersection-over-Union (IoU) gain of 17.6% and Dice score gain of 13.6% over freely available labels from OpenStreetMap. Finally, spatio-temporal building maps are generated to portray the applicability of this research.

CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement

Noisy situations cause huge problems for the hearing-impaired, as hearing aids often make speech more audible but do not always restore intelligibility. In noisy settings, humans routinely exploit the audio-visual (AV) nature of speech to selectively suppress background noise and focus on the target speaker. In this paper, we present a novel language-, noise- and speaker-independent AV deep neural network (DNN) architecture, termed CochleaNet, for causal or real-time speech enhancement (SE). The model jointly exploits noisy acoustic cues and noise robust visual cues to focus on the desired speaker and improve speech intelligibility. The proposed SE framework is evaluated using a first of its kind AV binaural speech corpus, ASPIRE, recorded in real noisy environments, including cafeteria and restaurant settings. We demonstrate superior performance of our approach in terms of both objective measures and subjective listening tests, over state-of-the-art SE approaches, including recent DNN based SE models. In addition, our work challenges a popular belief that scarcity of a multi-lingual, large vocabulary AV corpus and a wide variety of noises is a major bottleneck to build robust language, speaker and noise-independent SE systems. We show that a model trained on a synthetic mixture of the benchmark GRID corpus (with 33 speakers and a small English vocabulary) and CHiME 3 noises (comprising bus, pedestrian, cafeteria, and street noises) can generalise well, not only on large vocabulary corpora with a wide variety of speakers and noises, but also on completely unrelated languages such as Mandarin.

A Noise-Tolerant Zeroing Neural Network for Time-Dependent Complex Matrix Inversion Under Various Kinds of Noises

Complex-valued time-dependent matrix inversion (TDMI) is extensively exploited in practical industrial and engineering fields. Many current neural models are presented to find the inverse of a matrix in an ideal noise-free environment. However, the outer interferences are normally believed to be ubiquitous and avoidable in practice. If these neural models are applied to complex-valued TDMI in a noise environment, they need to take a lot of precious time to deal with outer noise disturbances in advance. Thus, a noise-suppression model is urgent to be proposed to address this problem. In this article, a complex-valued noise-tolerant zeroing neural network (CVNTZNN) on the basis of an integral-type design formula is established and investigated for finding complex-valued TDMI under a wide variety of noises. Furthermore, both convergence and robustness of the CVNTZNN model are carefully analyzed and rigorously proved. For comparison and verification purposes, the existing zeroing neural network (ZNN) and gradient neural network (GNN) have been presented to address the same problem under the same conditions. Numerical simulation consequences demonstrate the effectiveness and excellence of the proposed CVNTZNN model for complex-valued TDMI under various kinds of noises, by comparing the existing ZNN and GNN models.

Application of empirical mode decomposition in the field of polymer physics

Noisy data has always been a problem to the experimental community. Effective removal of noise from data is important for better understanding and interpretation of experimental results. Over the years, several methods have evolved for filtering the noise present in the data. Fast Fourier transform (FFT) based filters are widely used because they provide precise information about the frequency content of the experimental data, which is used for filtering of noise. However, FFT assumes that the experimental data is stationary. This means that: (i) the deterministic part of the experimental data obtained from a system is at steady state without any transients and has frequency components which do not vary with respect to time and (ii) noise corrupting the experimental data is wide sense stationary, that is, mean and variance of the noise does not statistically vary with respect to time. Several approaches, for example, short time Fourier transform (STFT) and wavelet transform-based filters, have been developed to handle transient data corrupted with nonstationary noise (mean and variance of noise varies with respect to time) data. Both these approaches provide time and frequency information about the data (time at which a particular frequency is present in the signal). However, these filtering approaches have the following drawbacks: (i) STFT requires identification of an optimal window length within which the data is stationary, which is difficult and (ii) there are theoretical limits on simultaneous time and frequency resolution. Hence, filtering of noise is compromised. Recently, empirical mode decomposition (EMD) has been used in several applications to decompose a given nonstationary data segment into several characteristic oscillatory components called intrinsic mode functions (IMFs). Fourier transform of these IMFs identifies the frequency content in the signal, which can be used for removal of noisy IMFs and reconstruction of the filtered signal. In this work, we propose an algorithm for effective filtering of noise using an EMD-based FFT approach for applications in polymer physics. The advantages of the proposed approach are: (i) it uses the precise frequency information provided by the FFT and, therefore, efficiently filters a wide variety of noise and (ii) the EMD approach can effectively obtain IMFs from both nonstationary as well as nonlinear experimental data. The utility of the proposed approach is illustrated using an analytical model and also through two typical laboratory experiments in polymer physics wherein the material response is nonstationary; standard filtering approaches are often inappropriate in such cases. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2011

Noise and vibration phenomena in aircraft wheel and brake systems.

There is a wide variety of noise and vibration phenomena in aircraft brake systems for which must be accounted in the design process of such systems. These phenomena include such modes as whirl and squeal, the latter of which can be quite different from its counterpart in automotive systems and has likewise received much less attention in the literature than its automotive counterpart. Consequently, an overview of such phenomena with representative results from simulations and experiments will be presented to highlight the differences. Complicating matters is that brake-induced vibration often involves strong coupling with the aircraft structure, thereby necessitating a system level understanding beyond the brake itself. This aspect poses a particular problem to a brake component supplier in how to ensure favorable noise and vibration behavior for the full aircraft system, particularly early in the development cycle. To accomplish this goal, Goodrich has developed a successful methodology combining simulation and testing to assess the noise and vibration behavior at an earlier stage. The methodology implements simulations to guide appropriate brake component tests that are more tractable for the brake supplier yet sufficiently mimic the full system environment. With the combined simulation and testing, a reduction in actual testing can be achieved while improving component noise and vibration performance.

Application of Multiparameter Optimization for Robust Product Design

Taguchi optimization or robust design is an effective way to balance rigorous statistical testing and compressed time schedules when engineering a product design. In an effort to define the specifications of the parameters that will eventually control the product output, it is necessary to first identify those parameters that will most likely have the greatest effect on the performance of the system. Once the parameters have been narrowed down, it is then the job of the testing to identify the levels of each parameter to ensure robust performance of the system. Without the efficiencies of the designed experiments described as robust design, it would be impossible within the constraints of a tight design schedule to properly optimize a system design for reliable performance. The usefulness of the Taguchi optimization is especially important when a design will be subjected to a wide variety of noises. It is for this reason that Taguchi techniques fit well in the world of paper handling product design. This paper discusses how these techniques were used to optimize a production printer finishing module. It steps through the process from identifying the parameters and setting up the test to the additional evaluation of parameters to meet the design constraints of the physical hardware.

On magnetostrictive materials and their use in adaptive structures

Magnetostrictive materials are routinely employed as actuator and sensor elements in a wide variety of noise and vibration control problems. In infrastructural applications, other technologies such as hydraulic actuation, piezoelectric materials and more recently, magnetorheological fluids, are being favored for actuation and sensing purposes. These technologies have reached a degree of technical maturity and in some cases, cost effectiveness, which justify their broad use in infrastructural applications. Advanced civil structures present new challenges in the areas of condition monitoring and repair, reliability, and high-authority actuation which motivate the need to explore new methods and materials recently developed in the areas of materials science and transducer design. This paper provides an overview of a class of materials that because of the large force, displacement, and energy conversion effciency that it can provide is being considered in a growing number of quasistatic and dynamic applications. Since magnetostriction involves a bidirectional energy exchange between magnetic and elastic states, magnetostrictive materials provide mechanisms both for actuation and sensing. This paper provides an overview of materials, methods and applications with the goal to inspire novel solutions based on magnetostrictive materials for the design and control of advanced infrastructural systems.

Multilevel nonlinear filters for edge detection and noise suppression

Two new classes of multilevel nonlinear filters are introduced for simultaneous edge detection and noise suppression, which the authors call a nested median filter/median averaging filter (NMF/MAF) pair and a delayed decision filter/embedded median trimmed filter (DDF/EMTF) pair. Median filters and median-related filter cause an edge shift in the presence of an impulse near the edge. The proposed filters reduce such edge shifting while suppressing impulsive as well as nonimpulsive noise. It is shown that at the noisy edge point the NMF and the DDF are substantially superior both theoretically and experimentally to the median filter, the /spl alpha/-TM filter, and the STM filter in two respects: (1) the output bias error and (2) the output mean square error. It is also shown that in the noisy homogeneous region (nonedge point), the bias errors of the MAF are zero and the output mean square errors of the MAF are substantially close to those of the optimized single-level filters: the averager, the median filter, and the min-max filter under Gaussian, Laplacian, and uniform noise, respectively. Test results confirm that the NMF/MAF pair and the DDF/EMTF structure are each robust in preserving sharp edges, inhibiting edge shifting, and suppressing a wide variety of noise. >