Passive acoustic monitors analyze sound signals emitted by seafloor gas bubbles to measure leakage rates. In scenarios with low-flux gas leaks, individual bubble sounds are typically non-overlapping. Measurement methods for these bubble streams aim to estimate the frequency peak of each bubble sound, which correlates with the bubble's size. However, the presence of ocean ambient noise poses challenges to accurately estimating these frequency peaks, thereby affecting the measurement of gas leakage rates in shallow sea environments using passive acoustic monitors. To address this issue, we propose a robust measurement method that includes a noise-robust sparse time-frequency representation algorithm and an adaptive thresholding approach for detecting bubble frequencies. We demonstrate the effectiveness of our proposed method using experimental data augmented with ocean ambient noise and ship-transit noise recorded from a bay area.
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http://dx.doi.org/10.1121/10.0025547 | DOI Listing |
Adv Neurobiol
November 2024
College of Physicians and Surgeons, Columbia University, New York, NY, USA.
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View Article and Find Full Text PDFNeural Netw
December 2024
School of Electronic Engineering, Xidian University, Xi'an 710071, China.
Single-channel speech enhancement primarily relies on deep learning models to recover clean speech signals from noise-contaminated speech. These models establish a mapping relationship between noisy and clean speech. However, considering the sparse distribution characteristics of speech energy across the entire time-frequency spectrogram, constructing the mapping relationship from noisy to clean speech exhibits significant differences in regions where speech energy is concentrated and non-concentrated.
View Article and Find Full Text PDFSensors (Basel)
July 2024
Department of Mechanical Engineering, Inha University, 100 Inharo, Mitchuholgu, Incheon 22212, Republic of Korea.
Deep learning (DL) models require enormous amounts of data to produce reliable diagnosis results. The superiority of DL models over traditional machine learning (ML) methods in terms of feature extraction, feature dimension reduction, and diagnosis performance has been shown in various studies of fault diagnosis systems. However, data acquisition can sometimes be compromised by sensor issues, resulting in limited data samples.
View Article and Find Full Text PDFJ Acoust Soc Am
April 2024
Key Laboratory of Underwater Acoustic Communication and Marine Information Technology Ministry of Education, Xiamen University, Xiamen, China.
Passive acoustic monitors analyze sound signals emitted by seafloor gas bubbles to measure leakage rates. In scenarios with low-flux gas leaks, individual bubble sounds are typically non-overlapping. Measurement methods for these bubble streams aim to estimate the frequency peak of each bubble sound, which correlates with the bubble's size.
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