Automating ultrasonic vocalization analyses: The WAAVES program

Abstract

Background Human emotion is a crucial component of drug abuse and addiction. Ultrasonic vocalizations (USVs) elicited by rodents are a highly translational animal model of emotion in drug abuse studies. A major roadblock to comprehensive use of USV data is the overwhelming burden to attain accurate USV assessment in a timely manner. One of the most accurate methods of analyzing USVs, human auditory detection with simultaneous spectrogram inspection, requires USV sound files to be played back 4% normal speed. New Method WAAVES (WAV-file Automated Analysis of Vocalizations Environment Specific) is an automated USV assessment program utilizing MATLAB’s Signal and Image Processing Toolboxes in conjunction with a series of customized filters to separate USV calls from background noise, and accurately tabulate and categorize USVs as flat or frequency-modulated (FM) calls. In the current report, WAAVES functionality is demonstrated by USV analyses of cocaine self-administration data collected over 10 daily sessions. Results WAAVES counts are significantly correlated with human auditory counts (r(48)=0.9925; p<0.001). Statistical analyses used WAAVES output to examine individual differences in USV responses to cocaine, cocaine-associated cues and relationships between USVs, cocaine intake and locomotor activity. Comparison with Existing Method WAAVES output is highly accurate and provides tabulated data in approximately 0.4% of the time required when using human auditory detection methods. Conclusions The development of a customized USV analysis program, such as WAAVES streamlines USV assessment and enhances the ability to utilize USVs as a tool to advance drug abuse research and ultimately develop effective treatments.

Automated detection of 50-kHz ultrasonic vocalizations using template matching in XBAT

“…Thus, proper hardware selection and optimization of the template library is important for the rapid detection of USVs. Nevertheless, detection speed is faster than human detection, which can take nearly ten times longer, i.e., 15-20s of human scoring time for every 1s of recorded data (Reno et al, 2013). …”

“…Specifically, files must be screened manually in order to detect and quantify the observed vocalizations—a process which can take 15-20 hours of human scoring time for every 1 hour of recorded data (Reno et al, 2013). These types of signal-detection problems are common in the bioacoustics field, and viable detectors have been developed for a number of other species (e.g., Brandes, 2008; Mohammad & McHugh, 2011; Chesmore & Ohya, 2004; Charif & Pitzrick, 2008; Mellinger & Clark, 1997).…”

“…These types of signal-detection problems are common in the bioacoustics field, and viable detectors have been developed for a number of other species (e.g., Brandes, 2008; Mohammad & McHugh, 2011; Chesmore & Ohya, 2004; Charif & Pitzrick, 2008; Mellinger & Clark, 1997). However, to the best of our knowledge, only one method for the automatic detection of USVs is being developed (Reno et al, 2013). …”

Alcohol enhances unprovoked 22–28kHz USVs and suppresses USV mean frequency in High Alcohol Drinking (HAD-1) male rats

“…Frequency-modulated (FM) 50–55 kHz and 22–28 kHz USV counts were quantified using the WAAVES algorithm as previously described [40]. Briefly, the WAAVES algorithm applies a set of conditions to define 50–55 kHz FM and 22–28 kHz USVs and to filter out noise elements.…”

Ethological concepts enhance the translational value of animal models

“…This is a very time consuming process as the number of calls can go from 500 to 1000 different elements in just one recording of 10 min. Recently, the first attempts to automate the analysis of ultrasonic sounds have been reported (Barker et al, 2014;Reno et al, 2013), which hopefully will initiate the further technical development of these automated tools.…”