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For sound localization, the binaural auditory system of a robot needs audio-motor maps, which represent the relationship between certain audio features and the position of the sound source. This mapping is normally learned during an offline calibration in controlled environments, but we show that using computational audiovisual scene analysis (CAVSA), it can be adapted online in free interaction with...
This paper presents a system that uses various simple auditory and visual features to achieve human-robot dialog scene analysis. Our scene analysis system is able to learn how many speakers are in the scenario, where the speakers are and who is currently speaking. Speakers are unknown in advance. A visual short-term-memory (STM) helps to memorize persons, even if they disappear from the camera's field...
We present a system for learning audiovisual integration based on temporal and spatial coincidence. The current sound is sometimes related to a visual signal that has not yet been seen, we consider this situation as well. Our learning algorithm is tested in online adaptation of audio-motor maps. Since audio-motor maps are not reliable at the beginning of the experiment, learning is bootstrapped using...
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