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

Autor:	Han, Danbinaerin, Repetto, Rafael Caro, Jeong, Dasaem
Rok vydání:	2023
Předmět:	Computer Science - Sound Computer Science - Information Retrieval Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted at 24th International Society for Music Information Retrieval Conference (ISMIR 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2308.02249 Zobrazit plný text záznamu View this record from Arxiv