Eavesdropping
Audience Interaction in Networked Audio Performance
A networked audio performance environment which mixes the moods of audience members via an artificial agent conductor to form a diverse acoustic ecology based on the auditory display of their mood data. Audience members input their moods to start the performance and a reinforcement learning engine offers participants the opportunity to improve the validity of audio-to-mood mapping.
Members
Jack Stockholm, Philippe Pasquier.
Research papers
Stockholm, J. & Pasquier, P. (2008). "Eavesdropping: Audience Interaction in Networked Audio Performance" ACM International Conference on Multimedia (ACM MM 2008), Vancouver, Canada, pages 559-568.
Stockholm, J. (2008). "Eavesdropping: Network Mediated Performance in Social Space." Leonardo Music Journal, 18 (2008): 55-58.
Stockholm, J. & Pasquier, P. (2009). "Reinforcement Learning of Listener Response for Mood Classification of Audio" Proceedings of the First International Workshop on Social Behavior in Music (in conjonction with the IEEE conference on Social Computing).