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