2021/01/01 by Brendan O'Connor, Théis Bazin, Simon Dixon +5
Computer Science · Engineering · Neuroscience · Psychology · #Architecture #Art #Artificial intelligence #Computational creativity #Computer science #Computer vision #Creativity #Deep learning #Image (mathematics) #Inpainting #Music Technology and Sound Studies #Music and Audio Processing #Neuroscience and Music Perception #Psychology #Resampling #Security token #Spectrogram #Speech recognition #Visual arts #cs.AI #cs.HC #cs.SD #eess.AS
paper · pdf · doi:10.30746/978-91-519-5560-5
published as Proceedings of the 1st Joint Conference on AI Music Creativity, 2020 (p. 10). Stockholm, Sweden: AIMC · In Proceedings of the 2020 Joint Conference on AI Music Creativity (CSMC-MuMe 2020), Stockholm, Sweden, October 15-19, 2020
openalex publication_date 2021/01/01 · arxiv created 2021/11/16 · arxiv updated 2021/11/17 · openalex created_date 2021/11/22 · openalex updated_date 2026/08/05
This paper presents TRoco, a generative algorithm for the composition of music based on jazz music theory and driven by an input of the desired degree of musical tension over time. A method for the abstraction and analysis of musical structures based on jazz theory is detailed, as well as the application of this method in a generative algorithm that produces chord sequences with a desired tension-release contour. Also presented is an example implementation of TRoco, where the position of a user in a virtual environment is used to drive generation.