Contributions

Last Publication

24/03/2025 1st Publication "A Computational Approach to Interaction Type Analysis of Music Therapy Improvisations" @ Music & Science

The picture shows the first page of the article ""A Computational Approach to Interaction Type Analysis of Music Therapy Improvisations""
First Page of the article "A Computational Approach to Interaction Type Analysis of Music Therapy Improvisations"

 

"Improvisation in music therapy is a highly complex and diverse form of creativity, offering a wide variety of musical information for music therapists to work with. To address this diversity in research and analysis, it is common to combine a wide range of interdisciplinary scientific approaches. Microanalysis methods in music therapy provide highly insightful results on a detailed musical level in musical improvisation but come at the cost of a time-consuming analysis procedure. The automation of these methods in machine learning environments and the use of the wealth of digitally obtainable musical information in clinical improvisations is highly promising for enabling the efficient use of microanalytic methods in clinical practice. In particular, assessment procedures – the systematic collection and analysis of client information to plan subsequent therapy sessions – can benefit greatly from a microanalytic insight into imitation patterns or entrainment processes as observable in musical instrument digital interface (MIDI) data. However, the automation of microanalytic methods poses a challenge in formalising analytical arguments while at the same time maintaining qualitative validity in a machine learning environment. This article provides an interdisciplinary theoretical framework for the microanalysis of musical data in clinical improvisation that is suitable for computational implementation, leading to the development of an automated analysis tool for further use in research and clinical practice. While a pilot application of the system presented in the article suggests general functionality, future challenges for the training of a supervised classification model have been identified that focus on the need for formalisation of microanalytic arguments and feature development to ensure qualitative validity."

to the article: doi.org/10.1177/20592043251329233

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Last Public Contribution

27/07/2025 HIGH-M at 13th European Music Therapy Conference (EMTC2025) in Hamburg

From July 22–27, 2025, we had the opportunity to present our work on the HIGH-M Project and the tool CAMII at the 13th European Music Therapy Conference (EMTC) in Hamburg. The conference, held under the theme “Bridges”, brought together music therapy professionals, researchers, and practitioners from over 40 countries to discuss current developments in theory and practice.

HIGH-M contributed two workshops at the music therapy assessment pre-conference, an assessment roundtable and a paper, through which we provided insights into the system architecture and design of CAMII, as well as shared first statistical findings on diagnostics in how depression influences musical interaction in clinical improvisation as well as how musical interaction changes during successfull music therapy.

A particular highlight was the invitation to participate in the Closing Ceremony panel discussion on Artificial Intelligence in Music Therapy. The discussion focused on the potentials and limitations of AI in this emerging field and offered valuable perspectives from both clinical and technological viewpoints.

In addition to our own contributions, the EMTC provided a valuable platform for interdisciplinary exchange. Engaging conversations with music therapists from across Europe highlighted the relevance of collaboration between music therapy, musicology, and music technology, and underlined the importance of co-developing meaningful and ethically grounded tools for clinical use.

We thank all those who attended our sessions, the conference organisers for their outstanding work, and our colleagues from THWS who made this experience particularly enriching.

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