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Visualizing the Body Language of a Musical Conductor using Gaussian Process Latent Variable Models: Creating a visualization tool for GP-LVM modelling of motion capture data and investigating an angle based model for dimensionality reduction
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this bachelors’ thesis we investigate and visualize a Gaussian

process latent variable model (GP-LVM), used to model

high dimensional motion capture data of a musical conductor

in a lower dimensional space.

This work expands upon the degree project of K. Karipidou,

”Modelling the body language of a musical conductor

using Gaussian Process Latent Variable Models”, in which

GP-LVMs are used to perform dimensionality reduction of

motion capture data of a conductor conducting a string

quartet, expressing four different underlying emotional interpretations

(tender, angry, passionate and neutral). In

Karipidou’s work, a GP-LVM coupled with K-means and

an HMM are used for classification of unseen conduction

motions into the aforementioned emotional interpretations.

We develop a graphical user interface (GUI) for visualizing

the resulting lower dimensional mapping performed

by a GP-LVM side by side with the motion capture data.

The GUI and the GP-LVM mapping is done within Matlab,

while the open source 3D creation suite Blender is used to

visualize the motion capture data in greater detail, which

is then imported into the GUI.

Furthermore, we develop a new GP-LVM in the same

manner as Karipidou, but based on the angles between the

motion capture nodes, and compare its accuracy in classifying

emotion to that of Karipidou’s location based model.

The evaluation of the GUI concludes that it is a very useful

tool when a GP-LVM is to be examined and evaluated.

However, our angle-based model does not improve the classification

result compared to Karipidou’s position-based.

Thus, using Euler angles are deemed inappropriate for this


Keywords: Gaussian process latent variable model,

motion capture, visualization, body language, musical conductor,

euler angles.

Place, publisher, year, edition, pages
2016. , 38 p.
National Category
Engineering and Technology
URN: urn:nbn:se:kth:diva-195692OAI: diva2:1045056
Available from: 2016-11-08 Created: 2016-11-08 Last updated: 2016-11-08Bibliographically approved

Open Access in DiVA

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