Bibliometric analysis reveals growing research trends in artificial intelligence and traditional music, indicating evolving thematic focuses over time.
This study aims to explore the intersection of artificial intelligence (AI) and traditional music through a bibliometric lens, identifying key trends, contributors, and research hotspots within this emerging interdisciplinary field. The bibliometric data were extracted from the Scopus database, which covers publications from 1974 to April 19, 2025, using keywords related to AI and traditional music. This study utilized BiblioMagika to analyze publication trends, prolific authors, contributing countries, source titles, keyword co-occurrence patterns, and collaboration networks. The results indicate a significant increase in research output on the intersection of artificial intelligence and traditional music since 2019. Over time, the thematic focus has evolved from early applications such as signal processing, genre classification, and feature extraction rooted in music information retrieval, to more sophisticated areas including generative AI composition, cultural modeling of indigenous musical forms, and multimodal human-AI interaction in performance and education contexts.
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He et al. (2025) studied this question.
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