Tools for AI Music Creatives : Mapping the field

Sammanfattning

Within the creative industries, such as visual arts and music, there has been a rise of AI implementations to solve various tasks, in each respective creative field. Implementations within the field of AI music creation have gained a lot of attention in recent years, due to the fact that many tools have become proficient in making music. Previously, there has been a lot of research dedicated to the algorithms behind these tools, but not as much to other software qualities that may be useful to both users of these tools, and developers of such tools to know. Hence, the focus of this thesis will be on completing a mapping of 6 established AI music creation tools, after a set of technical evaluation components. The mapping was carried out by a functional taxonomy. The results showcase that a majority of the tools implement DL algorithms, all data-sets are constructed differently, the majority apply user-friendly cloud-based environments for their tools, and that there was an equal divide between open-and closed source tools. The discussion chapter analyzes why developers have created the tools in a certain way, why potential developers should consider to implement a music creation tool with a DL algorithm, and why they should consider studying existing open-source tools, due to the knowledge and resources developers stand to gain from such a platform. Closed-source tools are more suitable for users who only want to create music with AI music creation tools, considering the uncomplicated usage, and access of such a tool.