  The rise of video-sharing platforms has attracted more and more people to
shoot videos and upload them to the Internet. These videos mostly contain a
carefully-edited background audio track, where serious speech change, pitch
shifting and various types of audio effects may involve, and existing audio
identification systems may fail to recognize the audio. To solve this problem,
in this paper, we introduce the idea of contrastive learning to the task of
audio fingerprinting (AFP). Contrastive learning is an unsupervised approach to
learn representations that can effectively group similar samples and
discriminate dissimilar ones. In our work, we consider an audio track and its
differently distorted versions as similar while considering different audio
tracks as dissimilar. Based on the momentum contrast (MoCo) framework, we
devise a contrastive learning method for AFP, which can generate fingerprints
that are both discriminative and robust. A set of experiments showed that our
AFP method is effective for audio identification, with robustness to serious
audio distortions, including the challenging speed change and pitch shifting.
