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

arXiv: https://arxiv.org/abs/2308.02249 · HTML: https://mlsanity.com/paper/2308.02249/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 29 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [CREPE: A Convolutional Representation for Pitch Estimation](https://mlsanity.com/paper/1802.06182/) (2018)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Representation Learning of Music Using Artist, Album, and Track Information](https://mlsanity.com/paper/1906.11783/) (2019)

## Built on by (most cited first, 5 of 5)

- [LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging](https://mlsanity.com/paper/2409.11264/) (2024)
- [Universal Music Representations? Evaluating Foundation Models on World Music Corpora](https://mlsanity.com/paper/2506.17055/) (2025)
- [PESTO: Real-Time Pitch Estimation with Self-supervised Transposition-equivariant Objective](https://mlsanity.com/paper/2508.01488/) (2025)
- [Towards cross-cultural study of folksong lyrics with machine translation](https://mlsanity.com/paper/2610.05084/) (2026)
- [Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations](https://mlsanity.com/paper/2610.15350/) (2026)

## Abstract (arXiv)

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.