Severe Noise Research Articles

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method.

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.

Local maximum synchrosqueezing reassigning chirplet transform and its application to gearbox fault diagnosis

Abstract Aiming at the problems of poor time-frequency aggregation and severe noise interference when traditional time-frequency analysis methods deal with complex multi-component signals, this paper proposes a new time-frequency analysis method – local maximum synchrosqueezing reassigning chirplet transform (LMSRCT). The core idea of the method is to introduce the principle of general linear chirplet transform (GLCT) into synchro-reassigning transform (SRT), followed by reassigning the results of local maximum synchrosqueezing chirplet transform (LMSSCT), and then introducing the innovative synchro-reassigning operator (SRO). This results to a novel three-step method for extracting instantaneous frequency, which ultimately yields the final time-frequency representation. This method ensures the integrity of each instantaneous frequency, solves the problem of energy divergence problems, and improves aggregation. Simulation and experimental results show that the LMSRCT can provide better characterization results compared to other methods and can effectively solve the different situations that occur between the instantaneous frequencies of complex signals. The proposed method can effectively estimate transmission speed, which achieves fault diagnosis under tacholess conditions, and the order analysis results are more accurate and reliable.

Human Scream Detection and Analysis for Controlling Crime Rate using Machine Learning and Deep Learning

Abstract: This paper addresses the problem of automatic detection and recognition of impulsive sounds, such as glass breaks, human screams, gunshots, explosions or door slams. A complete detection and recognition system is described and evaluated on a sound database containing more than 800 signals distributed among six different classes. Emphasis is set on robust techniques, allowing the use of this system in a noisy environment. The detection algorithm, based on a median filter, features a highly ro- bust performance even under important background noise conditions. In the recognition stage, two statistical classifiers are compared, using Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), respec- tively. It can be shown that a rather good recognition rate (98% at 70dB and above 80% for 0dB signal-to-noise ratios) can be reached, even under severe gaussian white noise degradations.

Phase denoising and unwrapping method for moiré fringes based on multiresolution analysis

Phase unwrapping for moiré fringes plays a crucial role in moiré tomography, and the precision of its outcomes deeply influences the detection results concerning high-temperature complex flow fields. In this paper, a phase denoising and unwrapping technique grounded in multiresolution analysis (MRA) is presented, and it is effectively applied to extract phase information from moiré fringes for propane-air flame and argon arc plasma flow fields. Firstly, the wrapped phase undergoes a wavelet packet decomposition for overall denoising. Subsequently, MRA is conducted to refine the denoising process and unwrap phase, involving layered wavelet packet decomposition and iterative unwrapping on different levels of approximate components. Following the hierarchical structure of MRA, the results from different levels are systematically combined and summarized. In order to ascertain the feasibility and applicability of this approach, a comparison is conducted between the results obtained from our proposed method and those obtained from three representative traditional methods. And then, the outcomes obtained from various unwrapping methods in scenarios involving noise and discontinuity, as well as the results between diverse wavelet basis functions, different decomposition layers and distinct iterations, are meticulously analyzed and compared. The findings indicate that our proposed method outperforms traditional phase unwrapping techniques in terms of continuity and smoothness, particularly when exposed to severe noise. Unlike the three classic methods, our proposed method maintains consistent results even in the presence of noise, thereby highlighting its advantages in terms of resilience against noise. In a word, this research serves as a crucial reference for the accurate detection of complex high-temperature flow fields by moiré tomography.

Joint Demosaicing and Denoising for Spike Camera

As a neuromorphic camera with high temporal resolution, spike camera can capture dynamic scenes with high-speed motion. Recently, spike camera with a color filter array (CFA) has been developed for color imaging. There are some methods for spike camera demosaicing to reconstruct color images from Bayer-pattern spike streams. However, the demosaicing results are bothered by severe noise in spike streams, to which previous works pay less attention. In this paper, we propose an iterative joint demosaicing and denoising network (SJDD-Net) for spike cameras based on the observation model. Firstly, we design a color spike representation (CSR) to learn latent representation from Bayer-pattern spike streams. In CSR, we propose an offset-sharing deformable convolution module to align temporal features of color channels. Then we develop a spike noise estimator (SNE) to obtain features of the noise distribution. Finally, a color correlation prior (CCP) module is proposed to utilize the color correlation for better details. For training and evaluation, we designed a spike camera simulator to generate Bayer-pattern spike streams with synthesized noise. Besides, we captured some Bayer-pattern spike streams, building the first real-world captured dataset to our knowledge. Experimental results show that our method can restore clean images from Bayer-pattern spike streams. The source codes and dataset are available at https://github.com/csycdong/SJDD-Net.

Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet

Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano.

Underwater object detection method based on learnable query recall mechanism and lightweight adapter.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.

Robust Graph Meta-Learning for Weakly Supervised Few-Shot Node Classification

Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. To efficiently learn with a small amount of data on graphs, meta-learning has been investigated in Graph ML. By transferring the knowledge learned from previous experiences to new tasks, graph meta-learning approaches have demonstrated promising performance on few-shot graph learning problems. However, most existing efforts predominately assume that all the data from the seen classes is gold labeled, yet those methods may lose their efficacy when the seen data is weakly labeled with severe label noise. As such, we aim to investigate a novel problem of weakly supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose Meta-GIN (Meta Graph Interpolation Network), a new graph meta-learning framework. Based on a new robustness-enhanced episodic training paradigm, Meta-GIN is meta-learned to interpolate node representations from weakly labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GIN over existing graph meta-learning studies on the task of weakly supervised few-shot node classification.

Enhanced adaptive high-frequency resonance technology for bearing fault detection

The traditional high-frequency resonance technology has two disadvantages: (1) The resonance frequency (RF) is obtained by using Fourier spectrum (FS) and the bandwidth is obtained based on experience, and the effect is often poor due to severe noise interference; (2) the separation of compound faults is not considered. After systematic research, a feasible fault detection methodology named enhanced adaptive high-frequency resonance technology is summarized and introduced, which is on the basis of power spectrum (PS) analysis, abbreviated as enhanced adaptive high-frequency resonance technique based on power spectrum (PS-EAHFRT), to realize the single fault detection and compound fault separation of bearing. Firstly, the theoretical values of various bearing fault modes are calculated to establish a database. Then, a series of spectral peaks are obtained by performing PS analysis, and each prominent spectral peak is considered as an alternative RF, that is, an alternative central frequency. Then, the bandwidth calculation formula of the bandpass filter is given, and the optimal bandwidth parameter is calculated by using the signal envelope kurtosis as a sparse measure. Finally, the original signal is denoised by the optimized band-pass filter, and then the filtered signal is demodulated by envelope PS analysis, and compared with the theoretical value in the library, and then the fault category is obtained. Simulated and real-world bearing cases are used to demonstrate the effectiveness and advantages of PS-EAHFRT. It is demonstrated that the PS-EAHFRT can effectively realize the bearing single fault detection and the compound fault separation. Moreover, the lower bound of the frequency interval in which this method can achieve bearing composite fault separation is explored.

Deep Selective Fusion of Visible and Near-Infrared Images Using Unsupervised U-Net.

In low light conditions, visible (VIS) images are of a low dynamic range (low contrast) with severe noise and color, while near-infrared (NIR) images contain clear textures without noise and color. Multispectral fusion of VIS and NIR images produces color images of high quality, rich textures, and little noise by taking both advantages of VIS and NIR images. In this article, we propose the deep selective fusion of VIS and NIR images using unsupervised U-Net. Existing image fusion methods are afflicted with the low contrast in VIS images and flash-like effect in NIR images. Thus, we adopt unsupervised U-Net to achieve deep selective fusion of multiple scale features. Due to the absence of the ground truth, we use unsupervised learning by formulating an energy function as a loss function. To deal with insufficient training data, we perform data augmentation by rotating images and adjusting their intensity. We synthesize training data by degrading clean VIS images and masking clean NIR images using a circle. First, we utilize pretrained visual geometry group (VGG) to extract features from VIS images. Second, we build an encoding network to obtain edge information from NIR images. Finally, we combine all features and feed them into a decoding network for fusion. Experimental results demonstrate that the proposed fusion network produces visually pleasing results with fine details, little noise, and natural color and it is superior to state-of-the-art methods in terms of visual quality and quantitative measurements.

LOQUAT: Low-Rank Quaternion Reconstruction for Photon-Counting CT.

Photon-counting computed tomography (PCCT) may dramatically benefit clinical practice due to its versatility such as dose reduction and material characterization. However, the limited number of photons detected in each individual energy bin can induce severe noise contamination in the reconstructed image. Fortunately, the notable low-rank prior inherent in the PCCT image can guide the reconstruction to a denoised outcome. To fully excavate and leverage the intrinsic low-rankness, we propose a novel reconstruction algorithm based on quaternion representation (QR), called low-rank quaternion reconstruction (LOQUAT). First, we organize a group of nonlocal similar patches into a quaternion matrix. Then, an adjusted weighted Schatten-p norm (AWSN) is introduced and imposed on the matrix to enforce its low-rank nature. Subsequently, we formulate an AWSN-regularized model and devise an alternating direction method of multipliers (ADMM) framework to solve it. Experiments on simulated and real-world data substantiate the superiority of the LOQUAT technique over several state-of-the-art competitors in terms of both visual inspection and quantitative metrics. Moreover, our QR-based method exhibits lower computational complexity than some popular tensor representation (TR) based counterparts. Besides, the global convergence of LOQUAT is theoretically established under a mild condition. These properties bolster the robustness and practicality of LOQUAT, facilitating its application in PCCT clinical scenarios. The source code will be available at https://github.com/linzf23/LOQUAT.

Assessment of Noise Levels of Churches in the Kpone Katamanso Municipal Assembly in Ghana.

Different sound levels are encountered by pastors, ministers, and the whole congregation during church services, which may extend for hours, and also by people living close to these churches. This can have an adverse effect on their health. The maximum allowable noise level in worship places is 65 dB (A), according to the Ghana Standards Authority (GSA). The aim of the study was to quantify the noise pollution levels of the churches in Kpone Katamanso Municipal Assembly (KKMA) and assess the equivalent noise levels of the churches' indoor and outdoor spaces. This investigation is being conducted due to the high number of churches and the noise emanating from these churches in the Kpone Katamanso Municipality and the possible impact on human health. Thirty churches were chosen at random for the study; on Sundays between the hours of 7.30 and 12.0 pm, sound levels in churches were measured using a portable General Class 1 Meter type DSM403SD with a data logging system. MS Excel was used to evaluate the data and determine characteristics including noise exposure levels (NEL), background noise level (L 90), severe noise level (L 10), and equivalent noise level (L eq). The indoor equivalent noise level ranges from 74.5 dB (A) to 104.1 dB (A), and the outdoor equivalent noise level ranges from 52.6 dB (A) to 85.3 dB (A). All of the noise levels found indoors of the churches were greater than the 65 dB (A) safe allowable limit, putting the congregants and residents at risk for a variety of physiological and psychological problems.

Stability Improvement of the TDLAS-Based CO Monitoring Module in a Coal Mine by Using a Spectral Denoising Algorithm Based on SVR

CO gas is not only lethal but also a significant forecasting indicator for the spontaneous combustion of coal mines. It is imperative that monitoring modules for CO gas that work well in the coal mine environment are available. A feasible solution is the detection of CO by using monitoring modules based on tunable diode laser absorption spectroscopy (TDLAS) over a mid-infrared waveband near 4.6 μm. However, in most cases, the mid-infrared TDLAS-based CO monitoring module tends to introduce severe interference fringe noise into the TDLAS spectral backgrounds which is difficult to filter out using traditional spectral filtering methods, reducing the detection performance of the module. In order to filter out the noise and improve the stability of the module in complex coal mine environments, this work proposed an algorithm based on support vector regression (SVR) to extract the TDLAS spectral backgrounds. Spectral analysis indicates that the TDLAS spectral background can be predicted over the entire scanning spectrum range by using this algorithm, and the noise in the spectral background can be effectively filtered out when calculating the absorbance spectrum based on the Lambert–Beer law. Compared to extracting spectral backgrounds using the traditional least square polynomial fit, the obtained correlation coefficients between regression models of spectral backgrounds and corresponding training point datasets were increased from below 0.998 to above 0.999. The peak-to-peak value of the obtained N2 absorbance spectrum was suppressed below 0.022 from nearly 0.045. The signal-to-noise ratio of the obtained 25 ppm CO absorbance spectrum was increased to 13.35 from 6.95. A CO monitoring module polluted by dust was used to conduct experiments to further test the SVR-based algorithm. The experiment results showed that after programming the SVR-based algorithm to the module, the estimated limit of detection of the module was reduced to 5.46 ppm from 29.08 ppm, and all the absolute measuring errors of the standard CO gases with different low concentrations were reduced to less than 4 ppm from a majority of the errors of more than 10 ppm, compared to least square polynomial fit. The CO monitoring module could still maintain the performance of high-precision quantitative detection when using the SVR-based algorithm even if it had been polluted severely. So, the CO monitoring module has good adaptability to harsh field environments, and its operation stability can be effectively improved by using the algorithm proposed in this work.

Research Progress on Identification and Suppression Methods for Monitoring the Cavitation State of Centrifugal Pumps

Cavitation is a detrimental phenomenon in hydraulic machinery, adversely impacting its performance, inducing vibration and noise, and leading to corrosion damage of overflow components. Centrifugal pump internal cavitation will lead to severe vibration and noise, and not only will the performance of hydraulic machinery be adversely affected but the impact generated by the collapse of the vacuole will also cause damage to the impeller wall structure, seriously affecting the safety of the equipment’s operation. To prevent the generation and development of internal cavitation in centrifugal pumps, to prevent the hydraulic machinery from being in a state of cavitation for a long time, to avoid the failure of the unit, and to realize the predictive maintenance of centrifugal pumps, therefore, it is of great significance to research the methods for monitoring the cavitation of hydraulic machinery and the methods for suppressing the cavitation. This paper comprehensively describes the centrifugal pump cavitation mechanism and associated hazards. It also discusses the current state of centrifugal pump cavitation monitoring methods, including commonly used approaches such as the flow-head method, high-speed photography, pressure pulsation method, acoustic emission method, and vibration method. A comparative analysis of these methods is presented. Additionally, the paper explores signal characterization methods for centrifugal pump cavitation, including time-domain feature extraction, frequency-domain feature extraction, and time–frequency-domain feature extraction. The current research status is elaborated upon. Moreover, the paper presents methods to mitigate cavitation and prevent its occurrence. Finally, it summarizes the ongoing research on identifying and determining the cavitation state in centrifugal pumps and offers insights into future research directions.

Association between occupational noise and obesity: a retrospective cohort study in China.

To determine the relationship between occupational noise, and obesity and body mass index (BMI) changes. Baseline data were collected from participants (n = 1264) who were followed for 6years in a retrospective study. The noise exposure level (LAeq,8h) was determined by equivalent continuous weighted sound pressure levels using the fixed-point surveillance method for noise monitoring. The cumulative noise exposure (CNE) level was determined using the equal energy formula, which is based on exposure history and level. The incidence of obesity at low (RR = 2.364, 95% CI 1.123-4.739]), medium (RR = 3.921, 95% CI 1.946-7.347]), high (RR = 5.242, 95% CI 2.642-9.208]), and severe noise levels (RR = 9.322, 95% CI 5.341-14.428]) was higher risk than the LAeq,8h control level. The risk of obesity among participants exposed to low (RR = 2.957, 95% CI 1.441-6.068]) and high cumulative noise levels (RR = 7.226, 95% CI 3.623-14.415]) was greater than the CNE control level. For every 1dB(A) increase in LAeq,8h, the BMI increased by 0.063kg/m2 (95% CI 0.055-0.071], SE = 0.004). For every 1dB(A) increase in the CNE, the BMI increased by 0.102kg/m2 (95% CI 0.090-0.113], SE = 0.006). Occupational noise is related to the incidence of obesity. The occupational noise level and occupational noise cumulative level were shown to be positively correlated with an increase in BMI.

RAPID AND AUTOMATED BODY MEASUREMENT OF CATTLE BASED ON STATISTICAL SHAPE MODEL

Abstract. The current methods of non-contact livestock body measurement directly deal with the low-quality point cloud data of livestock, which have low robustness and lack practicality. On the one hand, the success rate of keypoint detection for livestock body measurement is low. Due to the severe occlusion and noise in the point cloud data, body measurements of some data cannot be performed. On the other hand, the key frames need to be manually selected from the point cloud sequence during processing. Inspired by the work of 3D reconstruction based on animal statistical shape models, we implement the construction and learning of the statistical shape model of real cattle. Given the establishment of the statistical shape model of cattle, a 3D reconstruction and body measurement approach of real cattle based on low-quality point cloud data is proposed. Nine indicators are calculated and the overall estimation MAPE (Mean Absolute Percentage Error) is 10.27%. The whole process of the body measurement algorithm proposed in our paper can be extended to other quadrupeds.

Dark2Light: multi-stage progressive learning model for low-light image enhancement.

Due to severe noise and extremely low illuminance, restoring from low-light images to normal-light images remains challenging. Unpredictable noise can tangle the weak signals, making it difficult for models to learn signals from low-light images, while simply restoring the illumination can lead to noise amplification. To address this dilemma, we propose a multi-stage model that can progressively restore normal-light images from low-light images, namely Dark2Light. Within each stage, We divide the low-light image enhancement (LLIE) into two main problems: (1) illumination enhancement and (2) noise removal. Firstly, we convert the image space from sRGB to linear RGB to ensure that illumination enhancement is approximately linear, and design a contextual transformer block to conduct illumination enhancement in a coarse-to-fine manner. Secondly, a U-Net shaped denoising block is adopted for noise removal. Lastly, we design a dual-supervised attention block to facilitate progressive restoration and feature transfer. Extensive experimental results demonstrate that the proposed Dark2Light outperforms the state-of-the-art LLIE methods both quantitatively and qualitatively.

Bearing fault diagnosis of rail vehicles traction motor based on adaptive periodic segment matrix and singular value decomposition

The early fault characteristics of rolling bearings are relatively weak and accompanied by large amounts of noise, resulting in the collected bearing vibration signal having an extremely low signal-to-noise ratio, hence creating challenges in diagnosing faults in rail vehicle bearings. This paper proposes a method based on an adaptive period segment matrix and singular value decomposition to extract fault information from bearing fault impulse signals, in response to the problem of unstable period recognition in existing rolling bearing fault diagnosis methods due to strong background noise. Firstly, a periodic segment estimation method based on the average envelope spectrum is proposed. By calculating the envelope spectrum of the vibration signal, a method is proposed for assessing period length and period constraint F in segments. Then this method is applied to pinpoint the fault impact peak and the position of the vibration signal. Utilizing the fault impact peak and the calculated period constraint F, a strict rank-1 adaptive period segment matrix (APSM) is introduced. Finally, singular value decomposition is performed on this matrix, the bearing impulse vibration signal can be reconstructed through the first maximum singular value. The simulation and experimental analysis results of rolling bearings signals demonstrate the performance of the proposed method, even under severe background noise conditions(−20 dB), and the calculated envelope spectrum signal-to-noise ratio further supports the adaptive periodic segment matrix has an excellent ability to identify and extract periodic components, thus validating its usefulness as a fault diagnosis approach for rolling bearings.

Characteristics of community responses to airport noise around Bangkok International Airport

A social noise survey is a study of the community's response to noise. Data from a 2014-2015 noise survey of communities living around Bangkok International Airport is considered a spatial study that is important for the long-term planning of the airport development plan to prevent noise problems from aircraft operations. This paper presents the nature of survey data on airport noise from people living around Suvarnabhumi Airport over a decade after the airport's opening in 2006. It is now planned to expand the airport's capacity to handle more air traffic volume through runway expansion and passenger terminal expansion. The cross-sectional study is thought to be useful in future airport noise planning to avoid severe aircraft noise problems. In addition, it was an appropriate time to collect data from the field as a baseline for future airport noise management before expanding the capacity to handle more air traffic by adding more runways and passenger terminals.

Noise by neighbours: an update

RIVM conducts an annual survey of residents' perceptions of their living environment. This includes monitoring data on residents' perception of noise, odours and vibrations. Between 2019 and 2022, more than 8,000 Dutch residents aged 16 and over took part in these surveys. Online questionnaires presented people with a range of possible sources of annoyance and sleep disturbance in their homes and neighbourhoods. Road traffic noise is a major source of severe annoyance and sleep disturbance. Mopeds and motorcycles in particular cause severe noise annoyance. Compared to 2016, more people experience severe annoyance from the sound of vans. Road traffic also causes annoyance through vibrations. Neighbours are the second most important source of noise annoyance and the most important source of severe odour annoyance in the Netherlands. The latter is mainly due to odours from fireplaces, fire pits and barbecues. Neighbours' activities also cause severe annoyance due to vibrations.