How to Evaluate Audio Embedding Models for Sound Library Similarity Search

A technical guide published on DEV Community outlines a reusable evaluation protocol for selecting audio embedding models used in sound library similarity search. The article explains that two models can both return plausible results, making library-specific evaluation essential since benchmarks from other datasets do not reliably predict performance on your own recordings. It covers three pooling strategies — mean, max, and mean-plus-standard-deviation — each suited to different clip characteristics, from homogeneous short effects to long heterogeneous recordings. Three families of audio embeddings are discussed: supervised tagging models like YAMNet and PANNs, self-supervised models like OpenL3, and language-aligned models like CLAP, each encoding different audio properties and excelling in different retrieval scenarios. The guide deliberately avoids reporting model scores, emphasizing that the evaluation protocol itself is the transferable and practically useful output.