Abstract
•Introduces a method for the automatic detection of rat ultrasonic vocalizations.•Automatic scoring increases speed while retaining the opportunity for human review.•Open-sourced programming allows for collaboration among vocalization researchers.
Ultrasonic vocalizations (USVs) have been utilized to infer animals’ affective states in multiple research paradigms including animal models of drug abuse, depression, fear or anxiety disorders, Parkinson's disease, and in studying neural substrates of reward processing. Currently, the analysis of USV data is performed manually, and thus is time consuming.
The goal of the present study was to develop a method for automated USV recognition using a ‘template detection’ procedure for vocalizations in the 50-kHz range (35–80kHz). The detector is designed to run within XBAT, a MATLAB graphical user interface and extensible bioacoustics tool developed at Cornell University.
Results show that this method is capable of detecting >90% of emitted USVs and that time spent analyzing data by experimenters is greatly reduced.
Currently, no viable and publicly available methods exist for the automated detection of USVs. The present method, in combination with the XBAT environment is ideal for the USV community as it allows others to (1) detect USVs within a user-friendly environment, (2) make improvements to the detector and disseminate and (3) develop new tools for analysis within the MATLAB environment.
The present detector provides an open-source, accurate method for the detection of 50-kHz USVs. Ongoing research will extend the current method for use in the 22-kHz frequency range of ultrasonic vocalizations. Moreover, collaborative efforts among USV researchers may enhance the capabilities of the current detector via changes to the templates and the development of new programs for analysis.