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2013 | 123 | 6 | 1024-1028
Article title

Artificial Intelligence and Learning Systems Methods in Supporting Long-Term Acoustic Climate Monitoring

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EN
Abstracts
EN
Developing effective methods for automatic identification of noise sources is currently one of the most important tasks in long-term acoustical climate monitoring of the environment. Manual verification of recorded data, when it comes to proper determination of noise levels, is time-consuming and costly. A possible solution is to use pattern recognition techniques for acoustic signal recorded by a monitoring station. This paper presents usefulness of special directed measurement techniques, acoustic signal processing, and classification methods using artificial intelligence (the Sammon mapping) and learning systems methods (Support Vector Machines) in the recognition of corona audible noise from ultra-high voltage AC transmission lines.
Keywords
EN
Publisher

Year
Volume
123
Issue
6
Pages
1024-1028
Physical description
Dates
published
2013-06
Contributors
  • AGH - University of Science and Technology, Faculty of Mechanical Engineering and Robotics Department of Mechanics and Vibroacoustics, al. A. Mickiewicza 30, 30-059 Krakow, Poland
author
  • AGH - University of Science and Technology, Faculty of Mechanical Engineering and Robotics Department of Mechanics and Vibroacoustics, al. A. Mickiewicza 30, 30-059 Krakow, Poland
References
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Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv123n613kz
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