🤖 AI Summary
This study addresses the rapid yet uneven growth of artificial intelligence research in music, where attention across tasks lacks systematic measurement. To characterize this imbalance, the authors construct a joint taxonomy encompassing 12 application domains and 11 technical methodologies, analyzing 6,839 publications from 2015 to 2026. They propose a novel four-dimensional profile of research attention—comprising technical investment, method allocation, methodological diversity, and adoption lag of cutting-edge techniques—and apply bibliometric analysis combined with time-lag modeling. The findings reveal that generative tasks adopt state-of-the-art methods rapidly (average lag: 0.33 years), whereas applications in education, health, and governance exhibit substantial delays (4.33 and 5.00 years, respectively), highlighting significant disparities in research resource allocation and filling a critical gap in assessing equity within AI-driven music research.
📝 Abstract
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.