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 inspiration from the Contrastive Predictive Coding paradigm, we propose to train a Log-Mel-Spectrogram Transformer encoder to contrast observations at times separated by hypothesized 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 performance.

翻译：本文提出了一种新颖的自监督学习方案，用于训练节奏分析系统，并针对少样本节拍追踪任务进行了实例化。受对比预测编码范式的启发，我们提出训练一个对数梅尔频谱Transformer编码器，以对比由假设节拍间隔分隔的观测点与非分隔观测点。该方法无需真实节拍速度或节拍位置信息，而是依赖主导局部脉冲函数的局部最大值（作为Tatum位置的代理）来定义候选锚点、候选正样本（位于锚点2的幂次距离处）和负样本（其余时间位置）。实验表明，使用此方法在未标注的FMA、MTT和MTG-Jamendo数据集上进行预训练的模型，能够在少样本场景下（即仅使用少量标注示例）成功进行微调，并获得具有竞争力的节拍追踪性能。