I’m using AudioMoth devices (version 1.0.0) to record bird sounds in African savanna with settings of: 1-min recording length; 32-Htz sample rate (SR); “medium” gain, using; a “SanDisk Extreme” 32GB micro-SIM card with 1.5V Energizer Alkaline Batteries.
I’m trying to detect bird songs using the R packages warbleR (https://rpubs.com/marcelo-araya-salas/110155) &/or monitoR (https://rdrr.io/cran/bioacoustics/f/vignettes/tutorial.Rmd) using the following workflow. 1) identify “event windows” (i.e. a range of sound-attributes associated with the bird-call of interest, that may also include other similar bird calls), 2) extract 22 – 27 specific sound-attributes included in the event windows and label as bird of interest (yes) or not (no), 3) predict bird-calls of interest in other sound recordings using a Random Forest Model. I can use the workflow to make good predictions using bird-calls imported from Xeno-canto (https://www.xeno-canto.org/; 44 Hz SR), however I can’t use the same protocol to make similar predictions using my AudioMoth sound recordings.
From the sonograms (see attached; continuous time on x-axis, frequency on y-axis in Hz), it seems that the AudioMoth recordings have a poorer resolution and higher background noise than the Xeno-canto ones (even when the 44-Hz Xeno-canto calls are downscaled to 32-Hz). Could this causing the problem? Alternatively, perhaps there is an issue with using the AudioMoth .WAV files instead of .wav files in these packages?
Has anyone conducted similar analyses and experienced similar problems? Any help would be greatly appreciated. I am happy to share specific sound-clips and script if needed.
Thank you for your help!