DeepSqueak: a deep learning-based system for detection and analysis of ultrasonic vocalizations

By Kevin R. Coffey, Russell G. Marx, J. Neumaier. Neuropsychopharmacology, 2019

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Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires (2020, methodology): “When analyzed for temporal structure, mouse vocalizations are typically segmented into temporally-discrete USVs and then categorized into discrete clusters [1, 57, 76–78] in a manner similar to syllables of birdsong.”
Computational bioacoustics with deep learning: a review and roadmap (2021, methodology): “Solutions typically adapt well-known image object detector architectures such as YOLO and Faster R-CNN, which are quite different from the architectures used in other tasks (Venkatesh et al., 2021; Shrestha et al., 2021; Zseb˝ok et al., 2019; Coffey et al., 2019).”
Computational neuroethology: a call to action (2019, background): “…and mice; however, all of the described steps have been applied to other animals (like bacteria and birds) and other types of datastreams (like accelerometry or ultrasonic vocalizations) (Berg, 1975; Coffey et al., 2019; Markowitz et al., 2013; Van Segbroeck et al., 2017; Venkatraman et al., 2010).”