Motion capture data of a musical conductor's movements when conducting a string quartet is analysed in this work using the Gaussian Process Latent Variable Model (GP-LVM) framework. A dimensionality reduction on the high dimensional motion capture data to a two dimensional representation using a GP-LVM is performed, followed by classification of conduction movements belonging to different interpretations of the same musical piece. A dynamical prior is used for the GP-LVM, resulting in a representative latent space for the sequential conduction motion data. Classification results with great performance for some of the interpretations are obtained. The GP-LVM with dynamical prior distribution is shown to be a reasonable choice when wanting to model conduction data, opening up the possibility for creating for example a "conduct-your-own-orchestra" system in a principled mathematical way, in the future.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:kth-176101 |
Date | January 2015 |
Creators | Karipidou, Kelly |
Publisher | KTH, Datorseende och robotik, CVAP |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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