2014
DOI: 10.1016/j.jneumeth.2013.09.008
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Response to: Making WAAVES in the vocalization community: How big is the splash?

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Cited by 7 publications
(9 citation statements)
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“…Although this seems to be a reasonable prediction, acoustic parameters such as USV duration, mean, and peak frequency are rarely reported because of the difficulty in accurately calculating these parameters using manual assessment techniques. These problems are circumvented with the use of an automated MAT-LAB-based USV analyses program, such as our WAAVES algorithm (Reno and Duvauchelle, 2014;Reno et al, 2013) because the duration of each USV and all frequencies contributing to each USV are tabulated in the course of identifying each USV. In addition, the WAAVES algorithm can be programmed to identify the frequency at which the maximum power occurs within each USV.…”
Section: Discussionmentioning
confidence: 99%
“…Although this seems to be a reasonable prediction, acoustic parameters such as USV duration, mean, and peak frequency are rarely reported because of the difficulty in accurately calculating these parameters using manual assessment techniques. These problems are circumvented with the use of an automated MAT-LAB-based USV analyses program, such as our WAAVES algorithm (Reno and Duvauchelle, 2014;Reno et al, 2013) because the duration of each USV and all frequencies contributing to each USV are tabulated in the course of identifying each USV. In addition, the WAAVES algorithm can be programmed to identify the frequency at which the maximum power occurs within each USV.…”
Section: Discussionmentioning
confidence: 99%
“…Our recent development of a MATLAB-based algorithm (WAAVES) (Reno & Duvauchelle, 2014; Reno, Marker, Cormack, Schallert, & Duvauchelle, 2013) automates the tabulation of USV counts and acoustic characteristics, thereby allowing us to conduct long term studies exploring counts and acoustic characteristics of spontaneously emitted USVs over multiple recording sessions. Using this tool, we conducted a study focused just on P and NP rats (Reno et al, 2017) and found that alcohol-naïve P and NP rats can be distinguished based solely on the acoustic properties associated with 22 – 28 kHz USVs.…”
Section: Introductionmentioning
confidence: 99%
“…Although USV recordings are non-invasive and USV recording experiments are relatively easy to set up, USVs have remained relatively underutilized as important preclinical tools for the investigation of neural substrates underlying alcohol and drug abuse, primarily due to the manually intensive nature of the USV analysis process. The development of WAAVES (Reno & Duvauchelle, 2014; Reno et al, 2013), and other automated USV detection methods (Barker, Herrera, & West, 2014; Barker & Johnson, 2017) can help these problems and should encourage more labs to integrate USV recordings to their respective batteries of behavioral assays. Widespread use of USVs can unlock the true potential of this behavioral metric as a non-invasive correlate of underlying neural activity.…”
Section: Discussionmentioning
confidence: 99%
“…USV recordings were analyzed using the WAAVES program developed in our laboratory (Reno & Duvauchelle, 2014; Reno et al, 2013). This program reads USV containing audio files and produces a frequency spectrogram, which is subsequently analyzed using MATLAB’s Image Processing Toolbox (MathWorks, Inc., Natick, MA).…”
Section: Methodsmentioning
confidence: 99%
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