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In this paper, we introduce a computational analysis of the field recordingdataset of approximately 700 hours of Korean folk songs, which were recordedaround 1980-90s. Because most of the songs were sung by non-expert musicianswithout accompaniment, the dataset provides several challenges. To address thischallenge, we utilized self-supervised learning with convolutional neuralnetwork based on pitch contour, then analyzed how the musical concept of tori,a classification system defined by a specific scale, ornamental notes, and anidiomatic melodic contour, is captured by the model. The experimental resultshows that our approach can better capture the characteristics of tori comparedto traditional pitch histograms. Using our approaches, we have examined howmusical discussions proposed in existing academia manifest in the actual fieldrecordings of Korean folk songs.

Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song