Construction of an Innovative Model for Integrating AI-Enabled Folk Music into Vocal Music Teaching
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Abstract
As an important resource for vocal music teaching, folk music has long faced inheritance dilemmas and challenges in teaching integration. The development of artificial intelligence (AI) technology provides new possibilities for addressing this predicament. By constructing an intelligent teaching platform, AI realizes the digital collection, feature analysis, and personalized recommendation of folk music materials, organically embedding local musical elements into the vocal music curriculum system. Intelligent speech recognition and evaluation systems accurately capture the timbral characteristics, pitch variations, and ornamentation techniques of folk singing styles, providing learners with real-time feedback. Adaptive learning algorithms generate customized training programs based on students’ varying foundational levels, while virtual simulation technology reproduces the cultural scenarios of local music and enhances teaching immersion. From an engineering perspective, the model also involves acoustic signal processing, multimodal sensing, and wireless interactive teaching environments, which are relevant to intelligent audio transmission and digital education systems. This technology-empowered innovative model expands the content boundaries of vocal music teaching, builds a connection between traditional culture and modern education, and provides a cross-disciplinary framework for diversified artistic cultivation supported by AI-based signal analysis and intelligent teaching technologies.
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