AUTOMATIC IDENTIFICATION OF SPECIES FOR ADVANCED AND EFFICIENT RAPTOR SURVEYS: IMPROVING ACCURACY BY NEURAL NETWORKS AND NOISE REDUCTION
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- YAMAKAWA Masamichi
- 株式会社国土開発センター
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- MAE Masato
- 株式会社国土開発センター
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- KATAGIRI Toshimichi
- 株式会社国土開発センター
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- KANEDERA Noboru
- 石川工業高等専門学校
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- FUJII Retsu
- 石川工業高等専門学校
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- UENO Yusuke
- 石川県立大学 生物資源環境学部環境科学科
Bibliographic Information
- Other Title
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- 猛禽類調査の高度化・効率化を目指した鳴き声による種の自動判別:ニューラルネットワークとノイズリダクションによる精度向上
Abstract
<p> Surveys of raptor habitat are conducted in environmental impact assessments and natural environment surveys associated with development projects. In general, raptors are surveyed visually in the field to determine their habitat and nesting sites. However, because raptors are often alarmed when researchers enter the forest and because a large number of workers are required for surveys, efficient survey techniques must be developed. This study aimed to develop a technique for discriminating between five species of raptors (osprey, oriental honey buzzard, goshawk, grey-faced buzzard, and eastern buzzard) and the Japanese night heron by call. A sound analysis was conducted using deep neural networks after the noise reduction processing of sound data recorded near nesting forests of each species. As a result, a discrimination accuracy of more than 90% was achieved for five of the six species; however, there were also cases of misidentification resulting from environmental noise, indicating technical issues for further improvements in accuracy.</p>
Journal
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- Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research)
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Journal of Japan Society of Civil Engineers, Ser. G (Environmental Research) 77 (6), II_73-II_79, 2021
Japan Society of Civil Engineers
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Details 詳細情報について
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- CRID
- 1390854064974917248
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- NII Article ID
- 130008158736
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- ISSN
- 21856648
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- Text Lang
- ja
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- Data Source
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- JaLC
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed