“Find me more sounds like this one” is a nearest-neighbour query over vectors, and the hard part is not the search. It is that two embedding models will both return plausible results, and only a labelled evaluation on your own library tells you which one is answering the question you meant.
This page describes an evaluation protocol. It deliberately does not report scores for particular models: no such comparison was run here, and a benchmark number carried over from another corpus would not predict your result anyway, because retrieval quality on a sound library depends heavily on the recording conditions in that library. The protocol is the transferable part.
Frames to one vector per clip
Audio models produce a sequence, not a vector. YAMNet’s documented geometry gives one 1024-dimension embedding per 0.96-second patch at 50% overlap, so a 30-second clip yields about 61 of them. Similarity search needs exactly one vector per item, so something must collapse the sequence, and that collapse is a modelling decision rather than a formality.
Mean pooling describes the clip as a whole. It is the right choice when the clip is homogeneous — a two-second sound effect, a loop, a machine recording.
