Detecting shifts in coral reef soundscape with unsupervised learning

Biological sources contribute significantly to coral reef ambient noise environments, yet the ecosystem-level mechanisms of temporal and spatial variation in the reef soundscape are not well understood. In this study, subtle shifts in reef ambient noise are examined using unsupervised machine learning on hydrophone array data. Unsupervised learning does not require data labels, but uses nonlinear inference to find explanatory features within the data. A hydrophone array was used to generate spatially filtered time series inputs for machine learning. Video cameras were collocated and time-synced with the hydrophone array to provide nominal ground-truth. We discuss the tradeoff parameters of the unsupervised learning methods. Changes in the dominant data features during the experiment are compared to the video recordings and researcher observations.Biological sources contribute significantly to coral reef ambient noise environments, yet the ecosystem-level mechanisms of temporal and spatial variation in the reef soundscape are not well understood. In this study, subtle shifts in reef ambient noise are examined using unsupervised machine learning on hydrophone array data. Unsupervised learning does not require data labels, but uses nonlinear inference to find explanatory features within the data. A hydrophone array was used to generate spatially filtered time series inputs for machine learning. Video cameras were collocated and time-synced with the hydrophone array to provide nominal ground-truth. We discuss the tradeoff parameters of the unsupervised learning methods. Changes in the dominant data features during the experiment are compared to the video recordings and researcher observations.