Kevin Coffey has shared the following about DeepSqueak, a deep learning-based system for detection and analysis of ultrasonic vocalizations, which he developed with Russell Marx.
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs can be performed noninvasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have inhibited widespread adoption of vocalization analysis. Manual USV analysis is slow and laborious, while existing automated analysis software are vulnerable to broad spectrum noise routinely encountered in the testing environment.
To promote accessible and accurate USV research, we present “DeepSqueak”, a fully graphical MATLAB package for high-throughput USV detection, classification, and analysis. DeepSqueak applies state-of-the-art regional object detection neural networks (Faster-RCNN) to detect USVs. This dramatically reduces the false positive rate to facilitate reliable analysis in standard experimental conditions. DeepSqueak included pre-trained detection networks for mouse USVs, and 50 kHz and 22 kHz rat USVs. After detection, USVs can be clustered by k-means models or classified by trainable neural networks.