无监督训练模型，少量标注数据即可实现精准节拍追踪。
A Contrastive Self-Supervised Learning scheme for beat tracking amenable to few-shot learning
- 用对比学习让模型区分不同节拍间隔的音频片段。
- 在仅几例标注数据下，性能媲美传统方法。
- 适合标注数据稀缺的音乐分析场景。
本文提出一种新型自监督学习方案，用于训练节奏分析系统，并应用于少样本节拍追踪任务。受对比预测编码启发，设计了一个对数梅尔频谱图变换器编码器，通过对比假设节拍间隔内与非间隔内的音频观察来训练。无需真实节拍位置或速度信息，利用主导局部脉冲函数的局部极大值作为音符起点（Tatum）的代理，定义候选锚点、正样本（距离为2的幂次）和负样本（其余时间位置）。在未标注的FMA、MTT和MTG-Jamendo数据集上预训练后，该模型可在仅少量标注样本的情况下微调，获得具有竞争力的节拍追踪性能。
原文摘要 · Abstract (English)
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.
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