Autonomy Microanalysis (IAP-AM)
In a Nutshell
The Improvisation Assessment Profile - Autonomy Microanalysis (IAP-AM) method from Thomas Wosch is based on the Improvisation Assessment Profiles by Kenneth E. Bruscia and was first published in 2002. The main focus of the Autonomy Microanalysis lies within the Autonomy Profile, which was reduced in several ways.
Continuing the trend of Tony Wigram, who reduced the application of the IAPs to the use of the Variability and Autonomy Profile and less musical scales, Thomas Wosch includes only the scales "Rhythmical Basis, Melody and Timbre" to the analysis via the Autonomy Profile.
Operating with a hearing score, every change in interpersonal behaviour is located on the level of one of the three scales. An essential difference to Bruscias IAPs consists of recognizing every transition in interpersonal activity (continuous measurement) in the process of the improvisation instead of assigning a gradient to segments or whole improvisations.
Another development of IAP-AM is the observation of both the client and the therapist, which allows the analyst to examine the complete musical interaction of both musicians.
Role in HIGH-M
The Autonomy Microanalysis is the core of the HIGH-M project. The analysis of continuous musical data and the subsequent annotation in the form of the different gradients of the Autonomy Profile as being done by the Autonomy Microanalysis is the aim of our research project.
Of primary interest for us is the change of perspective which differentiates the Autonomy Microanalysis from the Improvisation Assessment Profiles. Instead of retrospectively assigning sequences or whole improvisations, the Autonomy Microanalysis focuses on the process of improvising and the dynamic development of the microsocial relations of the improvisers.
The main challenge in automating this method however consists of the high amount of individual ability and time necessary to create and analyse a hearing score. Furthermore, there are problems in translating the IAP-Autonomy gradients onto musical processes, as intuitively understood terms such as musical following or leading need to be readable in data-based environments.