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

Our last Contribution

Project Report HIGH-M (April 2024)

The following objectives of the HIGH-M project were achieved in early 2024:

  • The analysis method has been finalised and primary areas of work towards computational implementation have been identified
  • A segmentation prototype has been developed
  • The central dataset has been cleaned up and reorganised
  • Sample improvisations have been selected and analysed by Autonomy Microanalysis

Next, the following goals will be pursued:

  • A segmentation procedure including thresholds for the detection of musical motion will be developed and implemented
  • Autonomy Microanalysis processes are being compared to data based processing and both will be optimised and formalised
  • The first research paper will be written 

Our Partners

The logo of the Hochschule für Musik Nürnberg. By interacting, you will be forwarded to the HIGH-M pages of the Hochschule für Musik Nürnberg.
Hochschule für Musik Nürnberg
The logo of the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion. By interacting, you will be forwarded to the HIGH-M pages of RITMO.
RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion
The logo of the University of Jyväskylä. By interacting, you will be forwarded to the HIGH-M pages of the University of Jyväskylä.
University of Jyväskylä
The logo of the Anglia Ruskin University. By interacting, you will be forwarded to the HIGH-M pages of the Cambridge Institute for Music Therapy Research.
Cambridge Institute for Music Therapy Research