Nt! Good to hear you're making progress. Now you can label your events and then start "training" your Random Forest model with the variables of the autodetection. Therefore you first have to find out which variables will give the best results (this will take some trial and error to find the best results). Good luck!

Hello, Do you know where in the progress the problem starts? If it starts at step 1 of your workflow (finding the audio effents), you can try to change the sensitivity of the autodetection. Notice that in the tutorials of the packages they only use A-quality recordings from Xeno-Canto. So the parameters they use in the tutorials will work fine for recordings with similar quality. If you have recordings with more noise or calls that are not as loud, the autodetection will not detect the calls or only a small part of a call (making classification a lot more difficult). I'm using the bioacoustics package (threshold detection) in R to detect bat calls on my Audiomoth-recordings. Because bat calls are usually above the noise level this works fine. I've tried the same script (with different parameters) on some bird recordings that I've made with a Audiomoth. Because bird sounds are much lower and therefore more disturbed by background noise it was a lot harder to find the right parameters for the autodetection. I have not tried to classify this bird calls yet, but only finding them with the autodetection wasn't that easy. So the first step to get some result is to fine-tune the autodetection: run the autodetection with different parameters and compare the results in the data tables with the spectrograms of the recording to find the best parameters for the detection. Hope this will help. Greetings, Jan