Modelling the body language of a musical conductor using Gaussian Process Latent Variable Models
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Modellering av en dirigents kroppsspråk användandes Gaussian Process Latent Variable Models (Swedish)
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.
Place, publisher, year, edition, pages
2015. , 54 p.
Machine Learning, Gaussian Processes, Statistical Modelling, Classification, Motion Capture
IdentifiersURN: urn:nbn:se:kth:diva-176101OAI: oai:DiVA.org:kth-176101DiVA: diva2:866153
Master of Science in Engineering -Engineering Physics