Georgia Rossmann Martins
Research Subjects
Research Interests
Georgia Rossmann Martins has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Elisa Rubegni
School of Computing and Communication, Lancaster University, Lancaster, United Kingdom
1 shared publication - Leandro Soriano Marcolino
Lancaster University
1 shared publication - Mário Escarce Junior
University of California Santa Cruz, Santa Cruz, USA
1 shared publication
Latest Publications
- Open Access
- 29 September 2023
- Published by Association for Computing Machinery (ACM) in Proceedings of the ACM on Human-Computer Interaction
- Vol. 7 (CHI PLAY) , 665-698
The Aesthetics of Disharmony: Harnessing Sounds and Images for Dynamic Soundscapes Generation
This work presents an autonomous approach that explores the dynamic generation of relaxing soundscapes for games and artistic installations. Differently from past works, this system can generate music and images simultaneously, preserving human intent and coherency. We present our algorithm for the generation of audiovisual instances and also a system based on this approach, verifying the quality of the outcomes it can produce in light of current approaches for the generation of images and music. We also instigate the discussion around the new paradigm in arts, where the creative process is delegated to autonomous systems, with limited human participation. Our user study (N=74) shows that our approach overcomes current deep learning models in terms of quality, being recognized as human production, as if the outcome were being generated out of an endless musical improvisation performance.
- 29 September 2023
- Published by Association for Computing Machinery (ACM) in Proceedings of the ACM on Human-Computer Interaction
- Vol. 7 (CHI PLAY) , 665-698
Open AccessThe Aesthetics of Disharmony: Harnessing Sounds and Images for Dynamic Soundscapes Generation
This work presents an autonomous approach that explores the dynamic generation of relaxing soundscapes for games and artistic installations. Differently from past works, this system can generate music and images simultaneously, preserving human intent and coherency. We present our algorithm for the generation of audiovisual instances and also a system based on this approach, verifying the quality of the outcomes it can produce in light of current approaches for the generation of images and music. We also instigate the discussion around the new paradigm in arts, where the creative process is delegated to autonomous systems, with limited human participation. Our user study (N=74) shows that our approach overcomes current deep learning models in terms of quality, being recognized as human production, as if the outcome were being generated out of an endless musical improvisation performance.