Ongoing research on expressive musical performances.
The post-doctoral researchers (STMS - (Ircam/CNRS/Sorbonne Université/Ministère de la Culture) supervised by Elaine Chew present their current projects. This seminar will be in English and you can follow this discussion on our YouTube Channel: https://youtu.be/6kUxa8g_SLU
1- Accessible Musical Interactions for Novices. Emma Frid
This talk will explore interfaces for shaping expressive music performance and how such tools can promote access to music-making for musical novices with diverse abilities, research conducted within the « Accessible Digital Musical Instruments – Multimodal Feedback and Artificial Intelligence for Improved Musical Frontiers for People with Disabilities » Swedish Research Council project. The focus will be on ongoing and future work on interfaces allowing for accessible musical interactions through alternative representation and control.
2 - Introduction to EAR Stretch - Shaping tempo to enhance aesthetic reception of contemporary music. Emily Graber
Contemporary music (CM) extends and revolutionizes music from our past, yet many people are reluctant to engage with it and may actively dislike it. The Marie Skłodowska-Curie project EAR Stretch proposes to improve the reception of CM by allowing listeners to control CM tempo in real time with tapping, leveraging perception-action connections in the brain. I shall describe the planned upcoming experiment to investigate physiological responses to this embodied musical interaction.
3- Analysing and Visualising Ambiguous Structures in Performed Music through Bayesian Modeling and Dynamic Programming. Corentin Guichaoua
Music is predominantly analysed with a focus on score structures, which can lead to ambiguous answers, as evidenced by the diversity of analyses and performances of the same piece. A fundamental premise of this research is that hidden in any expressive performance is an analysis/interpretation of the music’s structures. I shall report on work-in-progress using dynamic programming and Bayesian modelling of performance parameters to provide probabilistic representations of structural ambiguity that allow us to more realistically model and reverse engineer performers’ conceptions of the score.
from left to right: Corentin Guichaoua Emily Graber Emma Frid