HIGH-M
Welcome to the Online-Presence of the HIGH-M Project (Human Interaction assessment and Generative segmentation in Health and Music)!
Located at the Institute of Applied Sciences (IFAS) of the Technical University of Applied Sciences Würzburg-Schweinfurt (THWS), we develop an automated tool to analyse autonomy - understood as types of social interaction in line with Kenneth E. Bruscia - of clinical improvisations. To do so, several theories for the analysis of musical improvisations and interaction are being synthesised, formalised, and automated. This tool is being developed for the analysis of clinical improvisations of people with diagnosed depressive disorder.
In development, we have set two aims for the tool. On the one hand, it is supposed to analyse specific dynamics of clinical and musical improvisations. On the other hand, it is also designed as a diagnostic tool in music therapy to analyse and recognise specifics of depressive musical interaction in clinical improvisations.
In our research project, we are being supported by several national and international partners who contribute besides the main data set their expertise in computational analysis, music information retrieval as well as cognitive and music therapeutic background. Furthermore, the THWS is a founding member of the International Music Therapy Assessment Consortium (IMTAC) and contributes to this via HIGH-M.
On the following pages, you can learn more about the structure, the state of our study, our partners, and our publications so far.
For further questions or information feel free to contact us.
Current Issues
10/10/2025 Third International Conference on Computational and Cognitive Musicology (ICCCM2025)
Last week, the HIGH-M had the pleasure of presenting at the Third International Conference on Computational and Cognitive Musicology (ICCCM2025). Our talk, “Integrating Music Therapy Assessment: From High-Level Reasoning to Computational Frameworks”, focused on how we can translate therapeutic reasoning into computational models for supervised machine learning — with the aim of supporting future interdisciplinary research on musical interaction in music therapy and musicology.
We are very thankful for the inspiring discussions and the opportunity to exchange perspectives across disciplines!
Our Partners
