Using the audio respiration signal for multimodal discrimination of expressive movement qualities

Abstract

In this paper we propose a multimodal approach to distinguish between movements displaying three different expressive qualities: fluid, fragmented, and impulsive movements. Our approach is based on the Event Synchronization algorithm, which is applied to compute the amount of synchronization between two low-level features extracted from multimodal data. In more details, we use the energy of the audio respiration signal captured by a standard microphone placed near to the mouth, and the whole body kinetic energy estimated from motion capture data. The method was evaluated on 90 movement segments performed by 5 dancers. Results show that fragmented movements display higher average synchronization than fluid and impulsive movements.