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Most of existing audio fingerprinting systems have limitations to be used forhigh-specific audio retrieval at scale. In this work, we generate alow-dimensional representation from a short unit segment of audio, and couplethis fingerprint with a fast maximum inner-product search. To this end, wepresent a contrastive learning framework that derives from the segment-levelsearch objective. Each update in training uses a batch consisting of a set ofpseudo labels, randomly selected original samples, and their augmentedreplicas. These replicas can simulate the degrading effects on original audiosignals by applying small time offsets and various types of distortions, suchas background noise and room/microphone impulse responses. In the segment-levelsearch task, where the conventional audio fingerprinting systems used to fail,our system using 10x smaller storage has shown promising results. Our code anddataset are available at \url{https://mimbres.github.io/neural-audio-fp/}.

Neural Audio Fingerprint for High-specific Audio Retrieval based on Contrastive Learning