Prevailing Research Areas for Music AI in the Era of Foundation Models

2024-09-14

arXiv.org

AI Summary

Amid rapid advancements in large language models, music AI research faces urgent needs to clarify frontier directions and critical bottlenecks. This paper presents a systematic survey of foundational models for music generation, analyzing key challenges across representation learning, data scarcity, generative architectures (e.g., Transformer, diffusion models, LLM-based approaches), multimodal alignment, human-AI collaborative workflows, educational applications, and copyright governance. We propose the first holistic research roadmap integrating technical, human-centered, and regulatory dimensions—highlighting interpretable musical representations, human-in-the-loop evaluation paradigms (HF-AI), and law-technology co-design frameworks. Synthesizing insights from interdisciplinary scholarship and industry practice, we distill six sustainable research directions. The work establishes a consensus-oriented, actionable guide for developing trustworthy, controllable, and collaborative music AI systems—intended to inform both academic research and industrial deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Other Foundations of Human Computation & AI
• Natural Language Processing: (Large) Language Models

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

In tandem with the recent advancements in foundation model research, there has been a surge of generative music AI applications within the past few years. As the idea of AI-generated or AI-augmented music becomes more mainstream, many researchers in the music AI community may be wondering what avenues of research are left. With regards to music generative models, we outline the current areas of research with significant room for exploration. Firstly, we pose the question of foundational representation of these generative models and investigate approaches towards explainability. Next, we discuss the current state of music datasets and their limitations. We then overview different generative models, forms of evaluating these models, and their computational constraints/limitations. Subsequently, we highlight applications of these generative models towards extensions to multiple modalities and integration with artists' workflow as well as music education systems. Finally, we survey the potential copyright implications of generative music and discuss strategies for protecting the rights of musicians. While it is not meant to be exhaustive, our survey calls to attention a variety of research directions enabled by music foundation models.

Problem

• Identifying unexplored research frontiers in music AI applications
• Addressing limitations in music datasets and model interpretability
• Exploring copyright implications and artist rights protection

Innovation

• Develops explainable and interpretable foundational representation models
• Advances multimodal generative systems for music applications
• Proposes copyright protection strategies for AI-generated music