Sound Localization for Humanoid Robots - Building Audio-Motor Maps based on the HRTF

Being able to locate the origin of a sound is important for our capability to interact with the environment. Humans can locate a sound source in both the horizontal and vertical plane with only two ears, using the head related transfer function HRTF, or more specifically features like interaural time difference ITD, interaural level difference ILD, and notches in the frequency spectra. In robotics notches have been left out since they are considered complex and difficult to use. As they are the main cue for humans' ability to estimate the elevation of the sound source this have to be compensated by adding more microphones or very large and asymmetric ears. In this paper, we present a novel method to extract the notches that makes it possible to accurately estimate the location of a sound source in both the horizontal and vertical plane using only two microphones and human-like ears. We suggest the use of simple spiral-shaped ears that has similar properties to the human ears and make it easy to calculate the position of the notches. Finally we show how the robot can learn its HRTF and build audiomotor maps using supervised learning and how it automatically can update its map using vision and compensate for changes in the HRTF due to changes to the ears or the environment.