This paper describes a method of applying a reinforcement learning artificial intelligence to categorize audio files by mood based on listener response during a performance. The system discussed is implemented in a performance art environment designed to present the moods of multiple participants simultaneously in a room via a diffusion of representative audio samples.
The author describes an Internet-based audio composition and diffusion system, Eavesdropping (2007–2008), designed for public spaces where several computer users are gathered, such as cafés. Compositions are created from abstract mood objects rather than musical structures. A composer uploads a set of audio files to represent the different moods in the composition. During a performance, a server-based Conductor selects audio files from this set to be played at each participant's laptop based on the composition, the number of participants in the room and the time they joined the performance. This project aims to enhance awareness of and connectedness among individual members of an audience at a generative musical performance by encouraging shared experiences.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.