Recent AI-driven step-function advances in several longstanding problems in music technology are opening up new avenues to create the next generation of music education tools. Creating personalized, engaging, and effective learning experiences are continuously evolving challenges in music education. Here we present two case studies using such advances in music technology to address these challenges. In our first case study we showcase an application that uses Automatic Chord Recognition to generate personalized exercises from audio tracks, connecting traditional ear training with real-world musical contexts. In the second case study we prototype adaptive piano method books that use Automatic Music Transcription to generate exercises at different skill levels while retaining a close connection to musical interests. These applications demonstrate how recent AI developments can democratize access to high-quality music education and promote rich interaction with music in the age of generative AI. We hope this work inspires other efforts in the community, aimed at removing barriers to access to high-quality music education and fostering human participation in musical expression.

翻译：近期，人工智能在音乐技术若干长期难题上取得的阶跃式进展，正为创建新一代音乐教育工具开辟新途径。在音乐教育领域，创建个性化、引人入胜且高效的学习体验是持续演进的挑战。本文通过两个案例研究，展示如何利用音乐技术的这些进展应对上述挑战。在第一个案例研究中，我们展示了一款应用，它利用自动和弦识别技术从音频轨道生成个性化练习，将传统听觉训练与真实音乐情境相连接。在第二个案例研究中，我们构建了自适应钢琴教程的原型，该教程利用自动音乐转录技术生成不同技能水平的练习，同时保持与音乐兴趣的紧密联系。这些应用展示了近期人工智能的发展如何能在生成式人工智能时代，普及高质量音乐教育的获取途径，并促进与音乐的深度互动。我们希望这项工作能激励学界开展更多努力，旨在消除获取高质量音乐教育的障碍，并促进人类参与音乐表达。