Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song
Danbinaerin Han Rafael Caro Repetto Dasaem Jeong
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.