In this paper, we introduce a computational analysis of the field recording dataset of approximately 700 hours of Korean folk songs, which were recorded around 1980-90s. Because most of the songs were sung by non-expert musicians without accompaniment, the dataset provides several challenges. To address this challenge, we utilized self-supervised learning with convolutional neural network based on pitch contour, then analyzed how the musical concept of tori, a classification system defined by a specific scale, ornamental notes, and an idiomatic melodic contour, is captured by the model. The experimental result shows that our approach can better capture the characteristics of tori compared to traditional pitch histograms. Using our approaches, we have examined how musical discussions proposed in existing academia manifest in the actual field recordings of Korean folk songs.

翻译：摘要：本文介绍了一种对约700小时韩国民歌现场录音数据集的计算分析，这些录音录制于20世纪80-90年代。由于大多数歌曲由非专业歌手无伴奏演唱，该数据集带来了诸多挑战。为应对这一挑战，我们基于音高轮廓采用卷积神经网络的自监督学习方法，进而分析模型如何捕获"tori"（一种以特定音阶、装饰音及惯用旋律轮廓界定的分类体系）这一音乐概念。实验结果表明，与传统音高直方图相比，我们的方法能更有效地捕捉tori的特征。通过我们的方法，我们考察了现有学术界提出的音乐理论如何在韩国民歌的实际现场录音中得以体现。