  Music discovery services let users identify songs from short mobile
recordings. These solutions are often based on Audio Fingerprinting, and rely
more specifically on the extraction of spectral peaks in order to be robust to
a number of distortions. Few works have been done to study the robustness of
these algorithms to background noise captured in real environments. In
particular, AFP systems still struggle when the signal to noise ratio is low,
i.e when the background noise is strong. In this project, we tackle this
problematic with Deep Learning. We test a new hybrid strategy which consists of
inserting a denoising DL model in front of a peak-based AFP algorithm. We
simulate noisy music recordings using a realistic data augmentation pipeline,
and train a DL model to denoise them. The denoising model limits the impact of
background noise on the AFP system's extracted peaks, improving its robustness
to noise. We further propose a novel loss function to adapt the DL model to the
considered AFP system, increasing its precision in terms of retrieved spectral
peaks. To the best of our knowledge, this hybrid strategy has not been tested
before.
