Evaluating changes in the marine soundscape of an offshore wind farm via the machine learning-based source separation

Oral presentation in Underwater Technology 2019 Kaohsiung

Evaluating changes in the marine soundscape of an offshore wind farm via the machine learning-based source separation

Tzu-Hao Lin1, Hsin-Te Yang2, Jie- Mao Huang2, Chiou-Ju Yao3, Yung-Shun Lien4, Pei-Jung Wang4, Fang-Yu Hu4

1Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Japan
2Observer Ecological Consultant, Taiwan
3Nature Museum of Natural Science, Taiwan
4Industrial Technology Research Institute, Taiwan

Investigating the ecological effects of offshore wind farms requires comprehensive surveys of marine ecosystem. Recently, the monitoring of marine soundscapes has been included in the rapid appraisals of geophysical events, marine fauna, and human activities. Machine learning is widely applied in acoustic research to improve the efficiency of audio processing. However, the use of machine learning to analyze marine soundscapes remains limited due to a general lack of human-annotated databases. In this study, we used unsupervised learning to recognize different sound sources underwater. We also quantified the temporal, spatial, and spectral variabilities of long-term underwater recordings collected near Phase I of the Formosa I wind farm. One source separation model was developed to recognize choruses made by fish and snapping shrimp, as well as shipping noise. Another model was developed to identify transient fish calls and echolocation clicks of marine mammals. Models were trained in an unsupervised manner using the periodicity-coded non-negative matrix factorization. After the sound sources were separated, events can be identified using Gaussian mixture models. Our information retrieval techniques facilitate future investigations of the spatiotemporal changes in marine soundscapes and allow to build an annotated database efficiently. The soundscape information can be used to evaluate the potential impacts of noise-generating activities on soniferous marine animals and their acoustic behavior before, during, and after the development of offshore wind farms.









The government devised the offshore wind promotion strategy with 3 phases, which include Demonstration Incentive Program, Zone Application for Planning, and Zonal Development. The demonstration incentive program was announced in 2012, and three projects have been selected accordingly. Then, the Directions of Zone Application for Planning (DZAP) was announced in 2015. More than 20 projects have applied for the DZAP, and they are required to pass environmental impact assessments (EIA) to obtain the first step approval for construction. However, the impact of underwater noise on marine mammal during construction and operation phases are very complex. The key issue is that the underwater noise monitoring method has no international standards and it is difficult to measure sufficient and valid data at sea. Moreover, EIA needs very detailed planning and comprehensive preparation to obtain data and analyze the impact assessment from underwater noise. More than 10 countries in Europe already installed offshore wind farms and employ different assessment methods for underwater noise impact. The requirement of the German government’s technical guidelines for underwater noise monitoring and assessment is most rigid and strict. Since 2008, the EU has proposed the “Good Environmental State (GES)” framework for the member states to achieve requirements for the offshore wind energy development monitoring and evaluation. The United States announced the Marine Mammal Acoustic Technical Guidance in 2016, which provides several acoustic thresholds on different species of marine mammal. This study will discuss the latest international technical guidelines for underwater noise monitoring and assessment. Finally, we carried out the report included the applicable method of underwater noise monitoring and assessment based on the development of offshore wind farms in Taiwan.

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