顶尖节拍追踪模型在SMC数据集上表现差，因误判节奏导致错误预测。
The SMC Blind Spot: A Failure Mode Analysis of State-of-the-Art Beat Tracking
- 分析三类失败模式：八度错、连续性错、完全失准。
- 21%的SMC曲目因最低速度限制被误判为双倍速度。
- 模型常给出高置信度但错误的节拍预测，适合改进节奏检测者参考。
过去二十年间，音乐节拍追踪从启发式检测算法发展为高性能深度神经网络（DNN）。尽管基于DNN的模型在主流打击乐数据集上接近完美，但对SMC数据集的表现始终不佳。通过测试先进模型在SMC各曲目上的节拍检测能力，我们识别出三种典型失败模式：八度错误、连续性错误，以及完全追踪失败（所有指标低于0.3）。研究发现，当前模型倾向于产生“自信但错误”的激活信号。此外，标准动态贝叶斯网络（DBN）默认最低节拍速度55 BPM，导致其无法正确推断21%的SMC曲目真实速度，迫使对慢速音乐做出双倍速度预测。这些根本性缺陷揭示了改进节拍与强拍检测的明确方向，尤其强调训练数据多样性与多假设速度估计的重要性。
原文摘要 · Abstract (English)
Over the past two decades, the task of musical beat tracking has transitioned from heuristic onset detection algorithms to highly capable deep neural networks (DNN). Although DNN-based beat tracking models achieve near-perfect performance on mainstream, percussive datasets, the SMC dataset has stubbornly yielded low F-measure scores. By testing how well state-of-the-art models detect beats on individual tracks in the SMC dataset, we identify three distinct failure modes: octave errors, continuity errors, and complete tracking failure where all metrics fall below 0.3. We reveal that state-of-the-art models tend to generate "confident-but-wrong" activations. Furthermore, we show that the standard DBN's default minimum tempo of 55 BPM prevents it from inferring the correct tempo for 21\% of SMC tracks, forcing double-tempo predictions on slow music. By exposing such fundamental oversights, we provide concrete directions for improving beat and downbeat detection, specifically emphasizing training data diversification and multi-hypothesis tempo estimation.
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