Capturing Expressive and Indicative Qualities of Conducting Gesture: An Application of Temporal Expectancy Models

  • Authors:

  • Dilip Swaminathan;Harvey Thornburg;Todd Ingalls;Stjepan Rajko;Jodi James;Ellen Campana;Kathleya Afanador;Randal Leistikow

  • Affiliations:

  • Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Arts, Media and Engineering, Arizona State University, USA;Zenph Studios Inc, Raleigh, USA

  • Venue:
  • Computer Music Modeling and Retrieval. Sense of Sounds
  • Year:
  • 2008

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Abstract

Many event sequences in everyday human movement exhibit temporal structure: for instance, footsteps in walking, the striking of balls in a tennis match, the movements of a dancer set to rhythmic music, and the gestures of an orchestra conductor. These events generate prior expectancies regarding the occurrence of future events. Moreover, these expectancies play a critical role in conveying expressive qualities and communicative intent through the movement; thus they are of considerable interest in musical control contexts. To this end, we introduce a novel Bayesian framework which we call the temporal expectancy modeland use it to develop an analysis tool for capturing expressiveand indicativequalities of the conducting gesture based on temporal expectancies. The temporal expectancy model is a general dynamic Bayesian network (DBN) that can be used to encode prior knowledge regarding temporal structure to improve event segmentation. The conducting analysis tool infers beat and tempo, which are indicative and articulation which is expressive, as well as temporal expectancies regarding beat (ictusand preparationinstances) from conducting gesture. Experimental results using our analysis framework reveal a very strong correlation in how significantly the preparation expectancy builds up for staccato vs legato articulation, which bolsters the case for temporal expectancy as cognitive model for event anticipation, and as a key factor in the communication of expressive qualities of conducting gesture. Our system operates on data obtained from a marker based motion capture system, but can be easily adapted for more affordable technologies like video camera arrays.