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Article

Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding

Department of Electrical and Electronic Engineering, University of West Attica, 12244 Athens, Greece
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Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1629; https://doi.org/10.3390/s19071629
Received: 26 February 2019 / Revised: 2 April 2019 / Accepted: 2 April 2019 / Published: 5 April 2019
(This article belongs to the Special Issue Sensors for Cultural Heritage Monitoring)
Cultural heritage sites, apart from being the tangible link to a country’s history and culture, actively contribute to the national economy, offering a foundation upon which cultural tourism can develop. This importance at the cultural and economic level, advocates for the need for preservation of cultural heritage sites for the future generations. To this end, advanced monitoring systems harnessing the power of sensors are deployed near the sites to collect data which can fuel systems and processes aimed at protection and preservation. In this paper we present the use of acoustic sensors for safeguarding cultural sites located in rural or urban areas, based on a novel data flow framework. We developed and deployed Wireless Acoustic Sensors Networks that record audio signals, which are transferred to a modular cloud platform to be processed using an efficient deep learning algorithm (f1-score: 0.838) to identify audio sources of interest for each site, taking into account the materials the assets are made of. The extracted information is presented exploiting the designed STORM Audio Signal ontology and then fused with spatiotemporal information using semantic rules. The results of this work give valuable insight to the cultural experts and are publicly available using the Linked Open Data format. View Full-Text
Keywords: acoustic sensors; cultural heritage; sensor signal processing; sensor linked data; deep learning; ontologies; semantic rules; state of preservation monitoring acoustic sensors; cultural heritage; sensor signal processing; sensor linked data; deep learning; ontologies; semantic rules; state of preservation monitoring
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MDPI and ACS Style

Kasnesis, P.; Tatlas, N.-A.; Mitilineos, S.A.; Patrikakis, C.Z.; Potirakis, S.M. Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding. Sensors 2019, 19, 1629. https://doi.org/10.3390/s19071629

AMA Style

Kasnesis P, Tatlas N-A, Mitilineos SA, Patrikakis CZ, Potirakis SM. Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding. Sensors. 2019; 19(7):1629. https://doi.org/10.3390/s19071629

Chicago/Turabian Style

Kasnesis, Panagiotis, Nicolaos-Alexandros Tatlas, Stelios A. Mitilineos, Charalampos Z. Patrikakis, and Stelios M. Potirakis 2019. "Acoustic Sensor Data Flow for Cultural Heritage Monitoring and Safeguarding" Sensors 19, no. 7: 1629. https://doi.org/10.3390/s19071629

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