Contributions

 

"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