DeepSqueak 1.0 was designed and tested with MATLAB 2017b.
To run DeepSqueak, navigate to the main DeepSqueak folder in MATLAB, and type "DeepSqeak" into the command line. DeepSqueak will add itself to the MATLAB path after running.
For help see the Wiki
Copyright © 2018 by Russell Marx & Kevin Coffey. All Rights Reserved.
DeepSqueak: A Deep Learning Based System for Quantification of Ultrasonic Vocalizations Kevin R. Coffey, Russell G. Marx, John F. Neumaier Psychiatry & Behavioral Sciences, University of Washington, Seattle, WA, 98104
Rodents engage in social communication through a rich repertoire of ultrasonic vocalizations (USVs). Recording and analysis of USVs has broad utility during diverse behavioral tests and can be performed non-invasively in almost any rodent behavioral model to provide rich insights into the emotional state and motor function of the test animal. Despite strong evidence that USVs serve an array of communicative functions, technical and financial limitations have been barriers for most laboratories to adopt vocalization analysis. Recently, deep learning has revolutionized the field of machine hearing and vision, by allowing computers to perform human-like activities like including seeing, listening and speaking. Such systems are constructed from biomimetic, 'deep', artificial neural networks. Here we present DeepSqueak, a USV detection and analysis software suite designed around cutting edge regional convolutional neural network architecture (Faster-RCNN), allowing it to perform human quality USV detection and classification automatically, rapidly and reliably. DeepSqueak was engineered to allow non-experts easy entry into USV detection and analysis yet is flexible and adaptable. DeepSqueak has a graphical user interface, is user friendly, flexible, feature packed, and systematically addresses the limitations of other available software. This package allows USV recording and analysis to be added inexpensively to existing rodent behavioral procedures, revealing a wide range of innate responses to provide another dimension of insights into behavior when combined with conventional outcome measures. DeepSqueak was designed specifically to reduce experiment costs, reduce analysis time, increase detection recall, reduce false positive rates, allow for automatic call classification and syntax analysis, all while retaining the option for manual analyses such as selection review and supervised classification.