  An ideal audio retrieval system efficiently and robustly recognizes a short
query snippet from an extensive database. However, the performance of
well-known audio fingerprinting systems falls short at high signal distortion
levels. This paper presents an audio retrieval system that generates noise and
reverberation robust audio fingerprints using the contrastive learning
framework. Using these fingerprints, the method performs a comprehensive search
to identify the query audio and precisely estimate its timestamp in the
reference audio. Our framework involves training a CNN to maximize the
similarity between pairs of embeddings extracted from clean audio and its
corresponding distorted and time-shifted version. We employ a channel-wise
spectral-temporal attention mechanism to better discriminate the audio by
giving more weight to the salient spectral-temporal patches in the signal.
Experimental results indicate that our system is efficient in computation and
memory usage while being more accurate, particularly at higher distortion
levels, than competing state-of-the-art systems and scalable to a larger
database.
