For bioacoustics there are significant problems with domain shift, label shift, label imbalance, and sample bias in training data. You need models to generalize

For bioacoustics there are significant problems with domain shift, label shift, label imbalance, and sample bias in training data. You need models to generalize to new data with very different noise profiles than the available training data, and handle significant intraclass variation. The gold standard for input features is a PCEN melspectrogram, largely because it gives useful generalizable features, through compression, normalization, and approximate log scaling of frequency features. Learned frontends tend to overfit training distributions badly - someone finally wrote this up recently, but I'm struggling to find the paper on my phone...