Machine learning for analysis of wind farm noise

Two challenges associated with analyzing acoustical data from wind farms are: 1) separating turbine sounds from environmental sounds and 2) classifying acoustical samples into different types of wind turbine noise based on acoustic characteristics. Machine-learning methods for classifying general environmental sounds have been developed using large human classified databases (e.g., YAMNet), but only a few studies have targeted classification of wind farm noise (WFN) specifically. Techniques for classifying wind farm noise have focused on identification of amplitude modulation (AM) using both traditional methods such as low frequency peak prominence (IOA method) and machine learning methods using both targeted AM acoustics features and more general deep acoustic features. To address these two challenges, we are developing a multi-echelon machine learning framework to identify and classify noise from wind farms using publicly available windfarm data and open-source software. The first echelon provides an automated method for identifying WFN samples that are free of environmental sounds. The second echelon uses machine learning to classify these wind farm noises according to the degree of AM, prominent tones, and other factors that might contribute to the human response and are incorporated in the metrics used or potentially used to assess compliance with regulations.