Hi Rob Good to hear that you’re also trying to analyse bird data using warbleR. It was taking me 5-6h to process ~ 3300 1min audio clips. I was using four parallel cores (using the “parallel” call to do this, say in the “autodect” function) on my computer, however I imagine you could quicken the processing time considerably if you have access to a larger processing computer by specifying more cores. I imagine that processing speed also relates to the size of your files, so this is a consideration too. Out of interest, how are you processing your bird calls? My aim is to build a model to predict specific bird calls within my sound clips (i.e. present / absent), using data from Xeno-Canto and my own recordings as training data. To date, I have been able to extract the relevant information using warbleR and then build a good Random Forest model which predicts well on the training data. My problem is that this model has poor predicative accuracy on my testing data; that is, the 3300 audio clips I want to predict calls on. I assume the problem relates to the fact that I am not including enough different types of “no” calls in my training data, which would explain why my model is performing well on my training, but not testing, data. Anyway, hope this helps Cheers Chris G
