A CONTRASTIVE SELF-SUPERVISED LEARNING SCHEME FOR BEAT TRACKING AMENABLE TO FEW-SHOT LEARNING

In this paper, we propose a novel Self-Supervised-Learning scheme to train rhythm analysis systems and instantiate it for few-shot beat tracking. Taking inspi-ration from the Contrastive Predictive Coding paradigm, we propose to train a Log-Mel-Spectrogram-Transformer-encoder to contrast observations at times separated by hy-pothesized beat intervals from those that are not. We do this without the knowledge of ground-truth tempo or beat positions, as we rely on the local maxima of a Predominant Local Pulse function, considered as a proxy for Tatum positions, to define candidate anchors, candidate positives (located at a distance of a power of two from the anchor) and negatives (remaining time positions). We show that a model pre-trained using this approach on the unlabeled FMA, MTT and MTG-Jamendo datasets can successfully be fine-tuned in the few-shot regime, i.e. with just a few annotated examples to get a competitive beat-tracking per-formance.