Designing meaningful audio for interactive applications such as video games or sonic user interfaces, presents designers with several challenges. One challenge is the fact that the perception of musical meaning involves cultural or learned aspects when it comes to tonality (Huron, 2006; Patel, 2008). For applied music and sound design, as cross-cultural phenomenon, this cultural specificity appears to be a significant disadvantage. Nevertheless, the history of interactive music in video games and sonic user interfaces illuminates many successful examples of meaningful musical icons in classic arcade games such as Pac-Man (1980), Donkey Kong (1981), Super Mario World (1990) and Pong (1972) and the user interface sounds of operating systems.
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This book seeks to communicate what we learned, what I learned, in the hope that readers (particularly musicians in training) can find ways to learn for themselves as they challenge themselves to try new, and different, things.
Concerns have been raised over the increased prominence ofgenerative AI in art. Some fear that generative models could replace theviability for humans to create art and oppose developers training generative models on media without the artist's permission. Proponents of AI art point to the potential increase in accessibility. Is there an approach to address the concerns artists raise while still utilizing the potential these models bring? Current models often aim for autonomous music generation. This, however, makes the model a black box that users can't interact with. By utilizing an AI pipeline combining symbolic music generation and a proposed sample creation system trained on Creative Commons data, a musical looping application has been created to provide non-expert music users with a way to start making their own music. The first results show that it assists users in creating musical loops and shows promise for future research into human-AI interaction in art.
