Harmonizing Tradition with Technology: Using AI in Traditional Music Preservation

Traditional music plays a unique role in preserving our history, connecting us to our roots, and fostering a sense of identity and continuity in a rapidly changing world. However, the inheritance of traditional music is extremely challenging due to the limited availability of literature and the small population of practitioners and audiences. In this paper, we investigated the possibility of using generative models in traditional music inheritance. In particular, we studied whether singing voice conversion (SVC) models are capable of producing high-quality Nanyin, an ancient and endangered music genre that can be found in the southern Fujian province of China. Our results show that SVC models can produce Nanyin audio with relatively acceptable quality based on our subjective tests. Furthermore, our objective evaluation results show that SVC models can effectively capture F0, which means that it can faithfully capture the original audio melody. They effectively retain low-frequency information in audio, ensuring consistency in pitch and rhythm, while some high-frequency details may be slightly inconsistent. Finally, we proposed an XGBoost regression based objective test algorithm that can automatically extract audio features and generate estimated Mean Opinion Scores (MOS) of SVC produced Nanyin audio based on predesigned metrics. Our work suggests a potential new approach in traditional vocal music preservation.