soundscapes.zip contains evaluation soundscape datasets from the BIRB benchmark (https://arxiv.org/abs/2312.07439), downsampled to 16kHz, preprocessed using CNN14 from PANNs (https://arxiv.org/abs/1912.10211), to select a 6-second window with the highest bird activation, and converted to Pytorch (.pt) format to facilitate usability for evaluating deep neural networks. These datasets are prepared specifically for bird sound classification. These preprocessed datasets are employed in the work "Domain-Invariant Representation Learning of Bird Sounds" (https://arxiv.org/abs/2409.08589), which assesss few-shot learning capabilities for transfer learning from focal to soundscape recordings. Dataset Structure Validation Dataset POW (pow.pt): Contains a dictionary with 'data' and 'label' keys representing the bird sounds and their corresponding labels. Source: https://zenodo.org/records/4656848#.Y7ijhOxudhE Test Datasets Each test dataset folder contains numerous subfolders, with each subfolder named according to an eBird species code to represent data for a specific bird species. SSW (ssw/): https://zenodo.org/records/7079380#.Y7ijHOxudhE NES (coffee_farms/): https://zenodo.org/records/7525349#.ZB8z_-xudhE UHH (hawaii/): https://zenodo.org/records/7078499#.Y7ijPuxudhE HSN (high_sierras/): https://zenodo.org/records/7525805#.ZB8zsexudhE SNE (sierras_kahl/): https://zenodo.org/records/7050014#.Y7ijWexudhE PER (peru/): https://zenodo.org/records/7079124#.Y7iis-xudhE Code and detailed instructions, including data loading, model implementation, and few-shot evaluation, can be found at: https://github.com/ilyassmoummad/ProtoCLR