论文标题
如果两个音乐版本不共享旋律，和谐，节奏或歌词怎么办？
And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?
论文作者
论文摘要
在过去的几年中，版本识别（VI）取得了很大的进步。一方面，公制学习范式的引入促进了可扩展而准确的VI系统的出现。另一方面，使用着专注于音乐作品的特定方面的功能，例如旋律，和谐或歌词，产生了可解释和有前途的表演。在这项工作中，我们基于这些最近的进步，并提出了一个基于公制的学习系统，该系统系统地利用了四个维度通常被承认传达版本之间的音乐相似性：旋律线条，谐波结构，节奏模式和歌词。我们描述了我们故意简单的模型体系结构，并特别表明，歌词的近似表示是有效地区分版本和非版本的有效代理。然后，我们描述了这些功能如何相互补充，并在两个公开可用的数据集上产生新的最新性能。我们最终建议，使用旋律，谐波，节奏和歌词功能的VI系统可以从理论上达到这些数据集中可获得的最佳性能。
Version identification (VI) has seen substantial progress over the past few years. On the one hand, the introduction of the metric learning paradigm has favored the emergence of scalable yet accurate VI systems. On the other hand, using features focusing on specific aspects of musical pieces, such as melody, harmony, or lyrics, yielded interpretable and promising performances. In this work, we build upon these recent advances and propose a metric learning-based system systematically leveraging four dimensions commonly admitted to convey musical similarity between versions: melodic line, harmonic structure, rhythmic patterns, and lyrics. We describe our deliberately simple model architecture, and we show in particular that an approximated representation of the lyrics is an efficient proxy to discriminate between versions and non-versions. We then describe how these features complement each other and yield new state-of-the-art performances on two publicly available datasets. We finally suggest that a VI system using a combination of melodic, harmonic, rhythmic and lyrics features could theoretically reach the optimal performances obtainable on these datasets.
