测试了模型在YouTube真实翻唱上的表现，发现效果远低于标准数据集。
On the Robustness of Cover Version Identification Models: A Study Using Cover Versions from YouTube
- 用多模态不确定性采样从YouTube收集翻唱数据
- 现有模型在新数据集上排名性能显著下降
- 识别器乐版等特定类型翻唱难度高，提供网络翻唱修改分类
近期的翻唱歌曲识别研究取得了显著进展，但模型通常在固定数据集上测试，依赖在线数据库SecondHandSongs。然而，这些数据集可能无法反映在线视频平台中翻唱的实际变化。本文通过多模态不确定性采样方法，从YouTube中选取并标注了一部分歌曲，评估了当前最先进的模型。结果显示，现有模型在新构建的数据集上排名性能明显低于社区数据集。此外，我们还分析了不同类型的翻唱版本（如器乐版）的表现，发现某些类型特别难以准确排序。最后，我们提出了一个网络翻唱版本中常见改动的分类体系。
原文摘要 · Abstract (English)
Recent advances in cover song identification have shown great success. However, models are usually tested on a fixed set of datasets which are relying on the online cover song database SecondHandSongs. It is unclear how well models perform on cover songs on online video platforms, which might exhibit alterations that are not expected. In this paper, we annotate a subset of songs from YouTube sampled by a multi-modal uncertainty sampling approach and evaluate state-of-the-art models. We find that existing models achieve significantly lower ranking performance on our dataset compared to a community dataset. We additionally measure the performance of different types of versions (e.g., instrumental versions) and find several types that are particularly hard to rank. Lastly, we provide a taxonomy of alterations in cover versions on the web.
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