New journal article from us!
"Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions" - a collaboration published in the Journal of the Royal Society Interface.
For machine learning, the main takeaway is that data augmentation is not just a way to create bigger training sets: used judiciously, it can mitigate the effect of confounds in the training data. It can also be used at test time to check a classifier's robustness.
For bioacoustics, the main takeaway is that previous automatic acoustic individual ID research may have been overconfident in their claimed accuracy, due to dataset confounds - and we provide methods to try and quantify such issues, even without gathering new data.
This journal article is the output of a nice collaboration we've been working on, to try and bring machine learning closer to solved the problems zoologists really need solved. It's been very pleasant working on these ideas with Pavel Linhart and Tereza Petrusková (I didn't actually meet Martin Šálek!). The problem of detecting individual animals' vocal signatures is not yet a solved one, but I hope this paper helps nudge us part of the way there, and helps the field to get there more efficiently by a careful use of audio datasets.