About the Project
Music is a very reach and complex stimulus and there are many of its characteristics that can have emotional consequences. Moreover, their effects need not be decoupled and some co-modulation could have amplifying emotional effects whereas others could counteract each other consequences. Moreover, any modification of music stream must respect its overall ‘musicality’ as otherwise the effect might be quite dissonant.
There is a need to investigate the effective ways of modulating the music and this project will investigate music as a multivariate stimulus with rich and varying temporal dependencies. Using machine learning tools, such as deep learning and models of complex temporal dependencies in multivariate data the project will investigate what are the most effective means of manipulating music for the optimal emotional effect. The starting point will be real music with known emotional content, but the machine learning tools may actually ultimately be used generatively, thus producing new synthetic music. The results will be evaluated in experiments with the music BCI thus leading to advancement of such technology.
The project will be hosted by the School of Biological Sciences, University of Reading. The University of Reading is one of the UK’s 20 most research-intensive universities and among the top 200 universities in the world. Achievements include the Queen’s Award for Export Achievement (1989) and the Queen’s Anniversary Prize for Higher Education (1998, 2006 and 2009). This project will take place in the Brain Embodiment Lab within Biomedical Engineering Section of the School of Biological Sciences (SBS), which has a strong reputation for its innovative research in cybernetics, and biomedical engineering, including Brain Computer Interfaces, animats - robots controlled by cultures of living neuronal cells and cognitive robotics systems.
For informal inquiries please contact Prof SJ Nasuto, email: [email protected].
"Applicants should have a bachelors (at least 2.1 or equivalent) or masters degree in physics,
applied mathematics, engineering, computing or a strongly related discipline.
Strong analytic and programming skills are preferable.
Experience in image processing and experimental data analysis are desirable."
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