Authors
Lidia J. Morris
Rebecca Leger
Michele Newman
John Ashley Burgoyne
Ryan Groves
Natasha Mangal
Jin Ha Lee
Date (dd-mm-yyyy)
2024
Title
Human-AI music process: A dataset of AI-supported songwriting processes from the AI Song Contest
Publication Year
2024
Publisher
San Francisco, CAISMIR
Document type
Conference contribution
Abstract
The advent of accessible artificial intelligence (AI) tools and systems has begun a new era for creative expression, challenging us to gain a better understanding of human-AI collaboration and creativity. In this paper, we introduce Human-AI Songwriting Processes Dataset (HAISP), consisting of 34 coded submissions from the 2023 AI Song Contest teams. This dataset offers a resource for exploring the complex dynamics of AI-supported songwriting processes, facilitating investigations into the possibilities and challenges posed by AI in creative endeavors. Overall, HAISP contributes to advancing understanding of human-AI co-creation from the users' perspective. Furthermore, we outline potential use cases for the dataset, ranging from analyzing AI tools utilized in songwriting to gaining insights into users' ethical considerations and expanding creative possibilities. This can help to inform both scholarly inquiry and practical applications in music composition and beyond.
Permalink
https://hdl.handle.net/11245.1/c90291af-fadf-4fd3-9a2b-77c9cd3fcd3c