Bharatanatyam Dance Transcription using Multimedia Ontology and Machine Learning
This work addresses the problem of documenting intangible cultural heritage for preservation and tutoring in Indian classical dance, though it appears incremental as it builds on existing ontology and transcription methods.
The paper tackles the challenge of preserving Bharatanatyam dance by developing a system that generates a parseable representation using multimedia ontology and machine learning, resulting in a transcription tool tested on recorded data for encoding performances into Labanotation.
Indian Classical Dance is an over 5000 years' old multi-modal language for expressing emotions. Preservation of dance through multimedia technology is a challenging task. In this paper, we develop a system to generate a parseable representation of a dance performance. The system will help to preserve intangible heritage, annotate performances for better tutoring, and synthesize dance performances. We first attempt to capture the concepts of the basic steps of an Indian Classical Dance form, named Bharatanatyam Adavus, in an ontological model. Next, we build an event-based low-level model that relates the ontology of Adavus to the ontology of multi-modal data streams (RGB-D of Kinect in this case) for a computationally realizable framework. Finally, the ontology is used for transcription into Labanotation. We also present a transcription tool for encoding the performances of Bharatanatyam Adavus to Labanotation and test it on our recorded data set. Our primary aim is to document the complex movements of dance in terms of Labanotation using the ontology.