The pop song generator: designing an online course to teach collaborative, creative AI
Yee-king, Matthew, Fiorucci, Andrea, d'Inverno, Mark
–arXiv.org Artificial Intelligence
This article describes and evaluates a new online AI-creativity course. The course is based around three near-state-of-the-art AI models combined into a pop song generating system. A fine-tuned GPT-2 model writes lyrics, Music-VAE composes musical scores and instrumentation and Diffsinger synthesises a singing voice. We explain the decisions made in designing the course which is based on Piagetian, constructivist 'learning-by-doing'. We present details of the five-week course design with learning objectives, technical concepts, and creative and technical activities. We explain how we overcame technical challenges to build a complete pop song generator system, consisting of Python scripts, pre-trained models, and Javascript code that runs in a dockerised Linux container via a web-based IDE. A quantitative analysis of student activity provides evidence on engagement and a benchmark for future improvements. A qualitative analysis of a workshop with experts validated the overall course design, it suggested the need for a stronger creative brief and ethical and legal content.
arXiv.org Artificial Intelligence
Jun-15-2023
- Country:
- Asia > China
- Hong Kong (0.04)
- Europe
- Finland (0.04)
- United Kingdom (0.04)
- North America > United States
- Virginia (0.04)
- Asia > China
- Genre:
- Instructional Material > Course Syllabus & Notes (1.00)
- Research Report (1.00)
- Industry:
- Education
- Leisure & Entertainment (1.00)
- Media > Music (1.00)