The CAMIL dataset is a unique set of audio recordings made with a realistic dummy head equipped with a binaural pair of microphones and mounted on a pan tilt robot setup. The dataset was gathered in order to investigate the audio-motor contingency from a computational point of view and experiments new models for sound localization based on machine learning. The recordings were made in November 2010 at INRIA Grenoble Rhône-Alpes and lead by Antoine Deleforge. A fully automatized protocol for the University of Coimbra’s audiovisual robot head POPEYE was designed to gather nearly 100,000 binaural sounds from all the robot’s motor states, with or without head movements. Records were made in the presence of a loud speaker emitting random spectrum sounds. Each record was annotated with the corresponding ground truth motor coordinates of the robot. The overall experiment was entirely unsupervised and laster 70 hours.
The CAMIL dataset is freely accessible for scientific research purposes and for non-commercial applications.