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

2025-08-14

AI Summary

This study addresses the challenge of irregular melodic motive structures in Korean folk songs and the low analytical efficiency stemming from reliance on manual annotation. We propose an automated motive boundary segmentation method based on fine-tuned speech transcription models, enabling large-scale quantitative analysis of oral musical traditions. Two structural features—motive count and duration entropy—are extracted and compared across social functional categories in a corpus of 856 folk songs. Results reveal that collective labor songs exhibit significantly higher motive structural complexity and greater rhythmic regularity than ritual or narrative songs, establishing the first systematic statistical association between melodic motive structure and social function. The approach overcomes the subjectivity and scalability limitations inherent in traditional ethnomusicological structural analysis, offering a transferable technical framework and empirically grounded methodology for computational humanities research on oral musical traditions.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Humans and AI: Crowd Sourcing and Human Computation
• Machine Learning: Structured Learning

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Automatic motive segmentation in Korean folk songs
• Quantifying structural features for oral music traditions
• Analyzing social function impact on song structure

Innovation

• Fine-tune speech model for motive segmentation
• Extract motif count and duration entropy
• Analyze structural features by social function