Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Eldwaik, Omar; Li, Francis F.

doi:10.3390/technologies6010019

Cited by 4 publications

(3 citation statements)

References 53 publications

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“…The basics of sound isolation lie in the design and placement of the monitor. Eldwaik and Li (2018) noted that wind noise was notoriously difficult to filter in an outdoor environment, due to its time‐varying nature and broadband frequency. We show that our LMF can help to reduce noise from the wind (Figure 8), but better still would be to not have the noise in the first place, achieved by having a microphone properly shielded from the wind.…”

Section: Discussionmentioning

confidence: 99%

Babbling brook to thunderous torrent: Using sound to monitor river stage

Osborne

Hodge

Love

et al. 2021

Earth Surf Processes Landf

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The passive, ambient sound above the water from a river has previously untapped potential for determining flow characteristics such as stage. Measuring sub-aerial sound could provide a new, efficient way to continuously monitor river stage, without the need for in-stream infrastructure. Previous published work has suggested that there might be a relationship between sound and river stage, but the analysis has been restricted to a narrow range of flow conditions and river morphologies. We present a method to determine site suitability and the process of how to record and analyse sound. Data collected along a 500 m length of the River Washburn during July 2019 is used to determine what makes a site suitable for sound monitoring. We found that sound is controlled by roughness elements in the channel, such as a boulder or weir, which influences the sound produced. On the basis of these findings, we collect audio recordings from six sites around the northeast of England, covering a range of flow conditions and different roughness elements, since 2019. We use data from those sites collected during storms Ciara and Dennis to produce a relationship between this sound and river stage. Our analysis has shown a positive relationship between an R 2 of 0.73 and 0.99 in all rivers, but requires careful site selection and data processing to achieve the best results. We introduce a filter that is capable of isolating a river's sound from other environmental sound. Future work in examining the role of these roughness elements is required to understand the full extent of this technique. By demonstrating that sound can operate as a hydrometric tool, we suggest that sound monitoring could be used to provide cost-effective monitoring devices, either to detect relative change in a river or, after more research, a reliable stage measurement.

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Section: Discussionmentioning

confidence: 99%

Babbling brook to thunderous torrent: Using sound to monitor river stage

Osborne

Hodge

Love

et al. 2021

Earth Surf Processes Landf

View full text Add to dashboard Cite

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“…However, these protections do not completely remove the effect of environmental factors, and some of them can still greatly affect the performance of the sound registration and analysis. Particularly important is the wind-induced noise produced by the interaction of the wind with the elements surrounding the microphone and the microphone itself, including the windscreen, as it can prevent the extraction of useful contextual information [41], [42]. Clearly, the microphones need to have state-of-the-art windshields, but these cannot fully compensate for the wind effects.…”

Section: E Audio Sensorsmentioning

confidence: 99%

“…Clearly, the microphones need to have state-of-the-art windshields, but these cannot fully compensate for the wind effects. Fortunately, different signal processing techniques can be used to isolate and mitigate them [43], [44], [41], [45]. Techniques using microphone arrays are especially effective [44], [46].…”

Section: E Audio Sensorsmentioning

confidence: 99%

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Thombre

Zhao

Ramm-Schmidt³

et al. 2022

IEEE Trans. Intell. Transport. Syst.

151

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Autonomous ships are expected to improve the level of safety and efficiency in future maritime navigation. Such vessels need perception for two purposes: to perform autonomous situational awareness and to monitor the integrity of the sensor system itself. In order to meet these needs, the perception system must fuse data from novel and traditional perception sensors using Artificial Intelligence (AI) techniques. This article overviews the recognized operational requirements that are imposed on regular and autonomous seafaring vessels, and then proceeds to consider suitable sensors and relevant AI techniques for an operational sensor system. The integration of four sensors families is considered: sensors for precise absolute positioning (Global Navigation Satellite System (GNSS) receivers and Inertial Measurement Unit (IMU)), visual sensors (monocular and stereo cameras), audio sensors (microphones), and sensors for remotesensing (RADAR and LiDAR). Additionally, sources of auxiliary data, such as Automatic Identification System (AIS) and external data archives are discussed. The perception tasks are related to well-defined problems, such as situational abnormality detection, vessel classification, and localization, that are solvable using AI techniques. Machine learning methods, such as deep learning and Gaussian processes, are identified to be especially relevant for these problems. The different sensors and AI techniques are characterized keeping in view the operational requirements, and some example state-of-the-art options are compared based on accuracy, complexity, required resources, compatibility and adaptability to maritime environment, and especially towards practical realization of autonomous systems.

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Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting

Wang,

Peng,

et al. 2023

IMA Journal of Management Mathematics

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One of the challenges of emergency ambulance demand (EAD) time series prediction lies in their non-stationary nature. We study this important problem and propose two hybrid forecasting models, which combine the Singular Spectrum Analysis (SSA) time-series technique with Autoregressive Integrated Moving Average (ARIMA) parameterized multivariate forecasting. Both daily and hourly time series are studied. The non-stationary time series are decomposed into three eigentriples by SSA: trends, periodic components and residuals. Selection of the group boundary point of the periodic component is a key issue in the SSA method. We use spectrum analysis to compute a threshold for maximum information content of periodic components. ARIMA mean value prediction models are employed to forecast the trends, periodic components and residuals sub-series. Our research compares ARIMA and SSA-based hybrid models by considering the emergency dispatching departure records of six core districts in Guangzhou city from January 1, 2021 to December 31, 2021. Results show that the integrated SSA-ARIMA model performs best. SSA is a very effective pre-processing method for non-stationary time series prediction. The predictive accuracy of using a hybrid model for hourly EAD time series is higher than that for daily ones. Our discussion should be useful for improving EAD prediction in contexts others than that considered in our research.

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Mitigating Wind Induced Noise in Outdoor Microphone Signals Using a Singular Spectral Subspace Method

Cited by 4 publications

References 53 publications

Babbling brook to thunderous torrent: Using sound to monitor river stage

Babbling brook to thunderous torrent: Using sound to monitor river stage

Sensors and AI Techniques for Situational Awareness in Autonomous Ships: A Review

Singular spectrum analysis (SSA) based hybrid models for emergency ambulance demand (EAD) time series forecasting

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