I am interested in class activation maps to interpret the results of classifications by a deep neural network, as shown in pages 173-180 (Figures 5.8 to 5.15) of the thesis of S. Kahl (2020) https://monarch.qucosa.de/api/qucosa%3A36986/attachment/ATT-0/
I have not been able to find the detailed methods they used to produce these maps in BirdNET. I did find some sources and repositories in GitHub to make class activation maps from images, but I would need more information on how to make them out of audio clips and through BirdNET.
Has anyone else attempted this? Thank you!