  Audio fingerprinting is a well-established solution for song identification
from short recording excerpts. Popular methods rely on the extraction of sparse
representations, generally spectral peaks, and have proven to be accurate,
fast, and scalable to large collections. However, real-world applications of
audio identification often happen in noisy environments, which can cause these
systems to fail. In this work, we tackle this problem by introducing and
releasing a new audio augmentation pipeline that adds noise to music snippets
in a realistic way, by stochastically mimicking real-world scenarios. We then
propose and release a deep learning model that removes noisy components from
spectrograms in order to improve peak-based fingerprinting systems' accuracy.
We show that the addition of our model improves the identification performance
of commonly used audio fingerprinting systems, even under noisy conditions.
