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.

本研究探讨了在基础模型研究进展背景下，音乐生成AI应用的研究空间。文章提出了生成模型的基础表示和可解释性的问题，评估了音乐数据集的现状及其局限性，并探讨了生成模型的应用与版权保护策略。研究为音乐AI领域的未来研究方向提供了重要见解。

基础模型时代音乐人工智能的主要研究领域