AI Stem Separation Tools in the Aural Skills Classroom
DOI:
https://doi.org/10.15763/issn.2994-7073.2026.39.39-58Keywords:
Artificial Intelligence, music theory pedagogyAbstract
Recent advancements in artificial intelligence and machine learning, particularly AI-powered stem separation tools, offer transformative potential for aural skills education. These tools can isolate individual audio components, such as vocals, bass, drums, and harmonic instruments, from mixed tracks, providing educators and students with novel ways to engage with complex musical textures. One of the key challenges in aural skills training is developing the ability to parse polyphonic textures, a skill known as auditory streaming. AI stem separation tools enable students to focus on individual musical elements, enhancing their ability to isolate and analyze components of multi-voice performances. This paper explores how separators can scaffold auditory streaming development and improve other aspects of aural skills instruction. It also addresses the ethical implications and responsibilities of using such technologies in the classroom. Through case studies, this work demonstrates how AI-driven tools can enrich both music instruction and student learning, while fostering awareness of technology’s broader impact on pedagogy.
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Copyright (c) 2026 Fred Hosken (Author)
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