Publication detail
Distributed Acoustic Sensing of Sounds in Audible Spectrum in Realistic Optical Cable Infrastructure
DEJDAR, P. MOKRÝ, O. MÜNSTER, P. HORVÁTH, T. SCHIMMEL, J.
Original Title
Distributed Acoustic Sensing of Sounds in Audible Spectrum in Realistic Optical Cable Infrastructure
Type
journal article in Web of Science
Language
English
Original Abstract
Distributed acoustic sensing (DAS) is an emerging technology with diverse applications in monitoring infrastructure, security systems, and environmental sensing. This study presents a dataset comprising acoustic vibration patterns recorded by a commercial DAS system, providing valuable insights into the acoustic sensitivity of optical fibers. The data are crucial for evaluating the performance of DAS systems, particularly in scenarios related to security and eavesdropping. The dataset offers the possibility to develop and test algorithms aimed at enhancing signal-to-noise ratio (SNR), detecting anomalies, and improving speech intelligibility. Additionally, this resource facilitates the validation of de-noising techniques through the calculation of the speech transmission index (STI). The experimental setup, measurement procedures, and the characteristics of the DAS system employed are comprehensively documented for researchers in the field of optical fiber sensing and signal processing.
Keywords
Distributed Acoustic Sensing; Fiber Optics; Fiber Sensors; Optical Communications; Speech Intelligibility Performance
Authors
DEJDAR, P.; MOKRÝ, O.; MÜNSTER, P.; HORVÁTH, T.; SCHIMMEL, J.
Released
13. 5. 2025
Publisher
Springer Nature
ISBN
2052-4463
Periodical
Scientific data
Year of study
12
Number
5
State
United Kingdom of Great Britain and Northern Ireland
Pages from
1
Pages to
8
Pages count
8
URL
Full text in the Digital Library
BibTex
@article{BUT197888,
author="Petr {Dejdar} and Ondřej {Mokrý} and Petr {Münster} and Tomáš {Horváth} and Jiří {Schimmel}",
title="Distributed Acoustic Sensing of Sounds in Audible Spectrum in Realistic Optical Cable Infrastructure",
journal="Scientific data",
year="2025",
volume="12",
number="5",
pages="1--8",
doi="10.1038/s41597-025-05119-0",
issn="2052-4463",
url="https://www.nature.com/articles/s41597-025-05119-0"
}