AI创新去向何方？衡量AI音乐领域的研究注意力失衡
Where Does AI Innovation Go? Measuring Research Attention Imbalance in AI Music
AI总结：
该研究通过分析6839篇AI音乐文献，构建含四项指标的研究注意力轮廓，发现AI音乐技术支持向内容导向任务集中，教育等领域滞后，揭示了领域发展不均衡问题。
AI中文摘要：
人工智能（AI）在音乐领域的快速发展，已将研究范畴从生成与信息检索拓展至教育、健康及治理领域。然而，这种发展并不必然意味着研究注意力的均衡分配：研究注意力在各类音乐任务中的投向何处？如何系统衡量这种失衡？现有研究多从技术、特定应用或文献计量学视角单独考察AI音乐，缺乏衡量领域层面失衡的系统框架。为填补这一空白，我们采用包含12个应用类别与11个技术方法族的联合分类体系，分析了2015年至2026年4月间的6839篇AI音乐出版物。我们提出研究注意力轮廓（Research Attention Profile），包含技术投入、方法分配、方法多样性及前沿方法采用滞后这四项指标。结果显示，技术支持集中于可扩展的内容导向任务，而教育、健康与治理领域仍支持不足；生成任务采用前沿方法的平均滞后仅0.33年，而教育领域为4.33年，健康领域则达5.00年。这些发现揭示了方法学发展的不均衡性，为构建更具社会响应性的AI音乐研究议程提供了支撑。
英文摘要：
The rapid growth of artificial intelligence (AI) in music has expanded research from generation and information retrieval to education, health, and governance. Yet this growth does not necessarily imply balanced research attention. Where is research attention directed across diverse music tasks, and how can such imbalance be systematically measured? Existing studies examine AI music from separate technical, application-specific, or bibliometric perspectives, but lack a systematic framework for measuring field-level imbalance. To address this gap, we analyze 6,839 AI music publications from 2015 to April 2026 using a joint taxonomy of 12 application categories and 11 technical method families. We propose the Research Attention Profile, comprising four indicators of technical investment, method allocation, methodological diversity, and frontier-method adoption lag. Results show that technical support is concentrated in scalable, content-oriented tasks, while education, health, and governance remain under-supported. Generation adopts frontier methods after only 0.33 years on average, compared with 4.33 years for education and 5.00 years for health. These findings reveal uneven methodological development and support a more socially responsive AI music research agenda.
