98%
921
2 minutes
20
This paper explores the wind noise reduction mechanism of porous microphone windscreens by investigating the spatial correlation of wind noise. First, the spatial structure of the wind noise signal is studied by simulating the magnitude squared coherence of the pressure measured with two microphones at various separation distances, and it is found that the coherence of the two signals decreases with the separation distance and the wind noise is spatially correlated only within a certain distance less than the turbulence wavelength. Then, the wind noise reduction of the porous microphone windscreen is investigated, and the porous windscreen is found to be the most effective in attenuating wind noise in a certain frequency range, where the windscreen diameter is approximately 2 to 4 times the turbulence wavelengths (2 < D/ξ < 4), regardless of the wind speed and windscreen diameter. The spatial coherence between the wind noise outside and inside a porous microphone windscreen is compared with that without the windscreen, and the coherence is found to decrease significantly when the windscreen diameter is approximately 2 to 4 times the turbulence wavelengths, corresponding to the most effective wind noise reduction frequency range of the windscreen. Experimental results with a fan are presented to support the simulations. It is concluded that the wind noise reduction mechanism of porous microphone windscreens is related to the spatial decorrelation effect on the wind noise signals provided by the porous material and structure.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1121/1.5021335 | DOI Listing |
J Acoust Soc Am
September 2025
Department of Physics, University of Louisiana at Lafayette, Lafayette, Louisiana 70503, USA.
A method is presented for determining the significant parameters, maximum wind speed and radius of maximum wind speed, of the surface winds associated with a hurricane. The method is based on Bayesian inversion, using Markov chain Monte Carlo sampling. Underwater acoustic measurements are used to estimate parameters in the axisymmetric Holland model for hurricane surface winds.
View Article and Find Full Text PDFJ Acoust Soc Am
September 2025
School of Ocean Engineering and Technology, Sun Yat-sen University, Zhuhai 519000, China.
This study establishes a quantitative framework using field observations and normal mode theory to reveal wind field control mechanisms over ambient noise vertical directionality in shallow water. Acoustic data from a vertical line array in the northern South China Sea, combined with sound speed profiles, seabed properties, and multi-source wind fields (ERA5 reanalysis/Weibull-distributed synthetics), demonstrate: (1) A 20-km spatial noise-energy threshold (>90% energy contribution), challenging conventional near-field assumptions (1-2 km); (2) frequency-dependent distribution: low-frequency (50-200 Hz) directionality depends on near-field sources, while high-frequency (>400 Hz) energy shifts seaward due to modal cutoff variations; (3) model validation shows 0.96 correlation at 100 Hz/100 km (stratified medium accuracy), but seabed interface waves induce 3.
View Article and Find Full Text PDFJ Air Waste Manag Assoc
September 2025
Department of Civil, Environmental & Construction Engineering, University of Central Florida, Orlando, Florida, USA.
The Integrated Mass Enhancement (IME) method is among the most popular remote sensing method for estimating methane emissions from point sources, and it has gained significant popularity in recent years. In this study, we evaluated how key environmental and observational factors, namely wind speed, instrument noise, terrain topography, and the source of 10-meter wind speed (U) data, influence emission estimates derived from the IME method. Although landfills are typically area sources, we used a simplified point-source emission setup as a controlled case to systematically explore the sensitivity of IME to each of these factors.
View Article and Find Full Text PDFSensors (Basel)
August 2025
School of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China.
Driven by the increasing global population and rapid urbanization, aircraft noise pollution has emerged as a significant environmental challenge, impeding the sustainable development of the aviation industry. Traditional noise prediction methods are limited by incomplete datasets, insufficient spatiotemporal consistency, and poor adaptability to complex meteorological conditions, making it difficult to achieve precise noise management. To address these limitations, this study proposes a novel noise prediction framework based on a hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention (CNN-BiLSTM-Attention) model.
View Article and Find Full Text PDFEntropy (Basel)
August 2025
School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China.
Sea surface wind speed is a key parameter in marine meteorology, navigation safety, and offshore engineering. Traditional marine radar wind speed retrieval algorithms often suffer from poor environmental adaptability and limited applicability across different radar systems, while existing empirical models face challenges in accuracy and generalization. To address these issues, this study proposes a novel wind speed retrieval method based on X-band marine radar image sequences and texture features derived from the Gray-Level Co-occurrence Matrix (GLCM).
View Article and Find Full Text PDF