The general approach can be applied anywhere and standalone though - use a foundational audio classifier to look at your unlabeled audio dataset! This approach is two-fold: first - we give the model a few sampels of an event or sound type we are looking for and determine to which class from the ones the model knows it belongs. It can be though of as encoding our sound events in the model "language".
After that we give larger samples that may or may not contain these events and tell the model to only react when those classes identified earlier are identified.
This way we can explore large audio datasets quickly. There is a good chance that some classifications are not exactly right - but it gives you a subset of you audio to actually take a look at - instead of having to listen though all of it!