Benchmark characterisation and automated detection of wind farm noise amplitude modulation
| Autor: | Kristy Hansen, Peter Catcheside, Branko Zajamsek, Bastien Lechat, Colin H. Hansen, Phuc D. Nguyen |
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| Rok vydání: | 2021 |
| Předmět: |
Acoustics and Ultrasonics
Computer science Calibration (statistics) Annoyance computer.software_genre 01 natural sciences Data set Amplitude modulation 03 medical and health sciences Noise 0302 clinical medicine Feature (computer vision) 0103 physical sciences Benchmark (computing) Data mining Limit set 030223 otorhinolaryngology 010301 acoustics computer |
| Zdroj: | Applied Acoustics. 183:108286 |
| ISSN: | 0003-682X |
| DOI: | 10.1016/j.apacoust.2021.108286 |
| Popis: | Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. Detection, quantification and characterisation of AM is relevant for regulatory bodies that seek to reduce adverse impacts of wind farm noise and for researchers and wind farm developers that aim to understand and account for this phenomenon. We here present an approach to detect and characterise AM in a comprehensive and long-term wind farm noise data set using human scoring. We established benchmark AM characteristics, which are important for validation and calibration of results obtained using automated methods. We further proposed an advanced AM detection method, which has a predictive power close to the practical limit set by human scoring. Human-based approaches should be considered as benchmark methods for characterising and detecting unique noise features. |
| Databáze: | OpenAIRE |
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