Exploring Relationships between Level of Mental Retardation and Features of Music Therapy Improvisations: A Computational Approach

Abstract

The present study sought to identify relationships between musical features of music therapy improvisations and clients' level of mental retardation, using a computationally-based method of analysis. 216 improvisations, contributed by 50 clients, were collected in MIDI format. Clients were divided into four groups according to their level of diagnosed mental retardation: 1 = none, 2 = mild, 3 = moderate, 4 = severe or profound. 43 client-related musical features were automatically extracted from their improvisations in the MATLAB computer environment and entered into a series of linear regression analyses as predictors of clients'level of mental retardation. The final model, which contained nine significant musical variables, accounted for 67% of the variation in clients' level of mental retardation. Specifically, level of mental retardation was best predicted by temporal elements of the music relating to note duration, note density, articulation, and amount of silence.

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“…The MTTB-method enables the automatic extraction of various musical features from an improvisation, as well as the examination of certain aspects of the therapist-client interaction. The method has been successfully utilized in the analysis of clinical improvisations by people with mental retardation [ 54 , 55 ]. For the analysis of digital audio data, a computational method called MIR (Music Information Retrieval) will be employed [ 56 ].…”

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“…These improvisations were analysed with Music Therapy Toolbox, and the relationships between the musical structure and the clients' diagnoses were investigated. It was found, for instance, that there are certain combinations of musical features that can predict a client's level of mental retardation (Luck et al 2006).…”

“…Topics of particular interest to the MCT include the physical characteristics of conductors' gestures, musician-conductor synchronization and the development of synchronization skills across the life-span. Luck and Toiviainen (2006), for example, examined precise temporal relations between the features of a conductor's gestures and musicians' performance in a real-world setting. The gestures of an expert conductor directing an ensemble were recorded using an optical motion-capture system, and various features of these gestures compared with the timing of the ensemble's performance.…”

“…As part of the research project Intelligent Music Systems in Music Therapy, the MCT has designed a tool for the analysis of music therapy improvisations (Luck et al 2006). This tool, called the Music Therapy Toolbox (MTTB), is based on the MIDI Toolbox and produces a set of quantitative descriptors representing the temporal and continuous evolution of a variety of musical features.…”

“…This tool, called the Music Therapy Toolbox (MTTB), is based on the MIDI Toolbox and produces a set of quantitative descriptors representing the temporal and continuous evolution of a variety of musical features. The toolbox has been used in particular in research on Music Therapy for the comparison of musical features that can be retrieved from the improvisations themselves with both diagnostic data about clients (Luck et al 2006) and continuous rating data evaluated by experts listening to the same improvisations ). The musical features extracted by the MTTB were chosen on the basis of the following criteria.…”