通过对基于综合和声音程的表示进行深度学习的和弦标签个性化
Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations
摘要：
自动和弦估计系统的准确性不断提高，大量异构参考注释的可用性以及注释者主观性研究的见解使得和弦标签个性化变得越来越重要。然而，自动和弦估计系统在历史上仅在单个参考注释上进行训练和评估。我们通过对基于和声音程的和弦标签表示进行深度学习来对主观性进行建模，从而引入自动和弦标签个性化的第一种方法。在集成了来自多个注释器的这些表示之后，我们可以从单个模型和注释器的和弦标签词汇表中准确地个性化各个注释器的和弦标签。此外，我们表明，和弦个性化使用多个参考注释优于使用一个单一的参考注释。
英文摘要：
The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
