A Real-Time Streaming and Detection System for Bio-acoustic Ecological Studies after the Fukushima Accident

Abstract : Acoustic ecology data have been used for a broad range of soundscape investigations. Counting sounds in a given soundscape is considered an effective method in ecology studies that provides comparative data for evaluating the impact of human community on the environment. In 2016, Kobayashi and Kudo collected a particularly valuable dataset containing recordings from within the exclusion (i.e., difficult-to-return-to) zone located 10 km from the Fukushima Daiichi Nuclear Power Plant in the Omaru District (Namie, Fukushima, Japan). These audio samples were continuously transmitted as a live stream of sound data from an unmanned remote sensing station in the area. In 2016, the first portion of their collected audio samples covering the transmitted sound recordings from the station was made available. Such data cover the bioacoustics in the area. This paper describes the methodologies by which we processed these recordings, in extreme conditions, as preliminary eco-acoustic indexes for demonstrating possible correlations between biodiversity variation and preexisting radioecology observations. The variations in some of these vocalizations were also studied.
Complete list of metadatas

Cited literature [19 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01879592
Contributor : Marion Poupard <>
Submitted on : Monday, September 24, 2018 - 10:17:35 AM
Last modification on : Monday, January 20, 2020 - 10:48:05 AM
Long-term archiving on: Tuesday, December 25, 2018 - 12:51:32 PM

File

A Real-Time Streaming and Dete...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01879592, version 1

Collections

Citation

Hill Kobayashi, Hiromi Kudo, Hervé Glotin, Vincent Roger, Marion Poupard, et al.. A Real-Time Streaming and Detection System for Bio-acoustic Ecological Studies after the Fukushima Accident. Multimedia Tools and Applications for Environmental & Biodiversity Informatics, 2018. ⟨hal-01879592⟩

Share

Metrics

Record views

109

Files downloads

215