Motive-level Analysis of Form-functions Association in Korean Folk song

TL;DR

This paper introduces an automatic method for segmenting motifs in Korean folk songs using a fine-tuned speech transcription model, enabling scalable analysis of structural features related to social functions.

Contribution

It presents a novel approach for motif segmentation in folk songs by fine-tuning speech models on lyric data, facilitating large-scale structural analysis.

Findings

Structural features vary systematically with social function.

Songs for collective labor differ from entertainment or personal songs.

The method enables scalable, quantitative analysis of oral music traditions.

Abstract

Computational analysis of folk song audio is challenging due to structural irregularities and the need for manual annotation. We propose a method for automatic motive segmentation in Korean folk songs by fine-tuning a speech transcription model on audio lyric with motif boundary annotation. Applying this to 856 songs, we extracted motif count and duration entropy as structural features. Statistical analysis revealed that these features vary systematically according to the social function of the songs. Songs associated with collective labor, for instance, showed different structural patterns from those for entertainment or personal settings. This work offers a scalable approach for quantitative structural analysis of oral music traditions.