A Machine Learning Approach for Detecting Wind Farm Noise Amplitude Modulation

Duc Phuc Nguyen; Kristy Hansen; Bastien Lechat; Peter Catcheside; Branko Zajamsek

doi:10.20944/preprints202012.0152.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 5 December 2020 / Approved: 7 December 2020 / Online: 7 December 2020 (12:51:54 CET)

Amplitude modulation (AM) is a characteristic feature of wind farm noise and has the potential to contribute to annoyance and sleep disturbance. This study aimed to develop an AM detection method using a random forest approach. The method was developed and validated on 6,000 10-second samples of wind farm noise manually classified by a scorer via a listening experiment. Comparison between the random forest method and other widely-used methods showed that the proposed method consistently demonstrated superior performance. This study also found that a combination of low-frequency content features and other unique characteristics of wind farm noise play an important role in enhancing AM detection performance. Taken together, these findings support that using machine learning-based detection of AM is well suited and effective for in-depth exploration of large wind farm noise data sets for potential legislative and research purposes.

Wind farm noise; Amplitude modulation; Random Forest; AM detection

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A Machine Learning Approach for Detecting Wind Farm Noise Amplitude Modulation

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