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

• Main Authors: Wei, Megan, Modrzejewski, Mateusz, Sivaraman, Aswin, Herremans, Dorien
• Format: Journal Article
• Language: English
• Published: 14-09-2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Multimedia; Computer Science - Sound
• DOI: 10.48550/arxiv.2409.09378

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.