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.

我们介绍了对大约 700 小时的 1980 年代至 1990 年代的韩国民歌野外录音数据集进行的计算分析。我们利用基于音高轮廓的卷积神经网络进行自监督学习，分析了模型对 tori 音乐概念的捕捉能力，实验结果表明我们的方法相比传统音高直方图更好地捕捉了 tori 音乐特征。利用我们的方法，我们研究了现有学术界提出的音乐讨论如何在韩国民歌的实际野外录音中体现。