6533b832fe1ef96bd129a276
RESEARCH PRODUCT
Predicting Music Therapy Clients’ Type of Mental Disorder Using Computational Feature Extraction and Statistical Modelling Techniques
Kari RiikkiläGeoff LuckPetri ToiviainenJaakko ErkkiläOlivier Lartillotsubject
Music therapymedia_common.quotation_subjectMusicalbehavioral disciplines and activitiesDevelopmental psychologylaw.inventionSilenceDiscriminant function analysislawPerceptionCLARITYCognitive dissonanceMusic information retrievalPsychologyCognitive psychologymedia_commondescription
Background. Previous work has shown that improvisations produced by clients during clinical music therapy sessions are amenable to computational analysis. For example, it has been shown that the perception of emotion in such improvisations is related to certain musical features, such as note density, tonal clarity, and note velocity. Other work has identified relationships between an individual’s level of mental retardation and features such as amount of silence, integration of tempo with the therapist, and amount of dissonance. The present study further develops this work by attempting to predict music therapy clients’ type of mental disorder, as clinically diagnosed, from their improvisatory material.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-01-01 |