Using automated recorders and occupancy models to monitor common forest birds across a large geographic region

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Passive acoustic monitoring as a potential tool to survey animal and ecosystem processes in freshwater environments (2019, methodology): “The use of passive acoustic monitoring (PAM) as a non‐invasive ecoacoustic method to sample and monitor ecosystems is gaining traction globally (Blumstein et al., 2011; Furnas & Callas, 2015; Felisberto et al., 2015; Heinicke et al., 2015; Gibb, Browning, Glover‐Kapfer, & Jones, 2018; 2010).”
Sound Event Localization and Detection of Overlapping Sources Using Convolutional Recurrent Neural Networks (2018, methodology): “number of CNN, RNN, and FC layers (0 to 4) and nodes (in the set of [16, 32, 64, 128, 256, 512]).”
Automatic acoustic detection of birds through deep learning: The first Bird Audio Detection challenge (2018, methodology): “…and so with modern remote monitoring stations able to capture continuous audio recordings the prospect opens up of massive- scale spatio- temporal monitoring of birds (Aide et al., 2013; Frommolt, 2017; Furnas & Callas, 2015; Hill et al., 2017; Knight et al., 2017; Matsubayashi et al., 2017).”
Comparing the sampling performance of sound recorders versus point counts in bird surveys: A meta‐analysis (2018, background): “Better sampling coverage across large spatial and temporal scales is demanded (Haughland et al., 2010), and autonomous sound recorders have already been successfully deployed across large regions (Furnas & Callas, 2015).”