Improving the Localization Accuracy of Virtual Sound Source through Reinforcement Learning

Localization of virtual sound source is a technology that allows the reproducing of three-dimensional sounds using stereo earphones, and applications of this technology in Brain-machine interface that use auditory stimuli are being investigated. In order to achieve virtual sounds using this technology, the Head-related Transfer Function (HRTF) of the user must be measured accurately. The HRTF can be measured accurately with the appropriate placement of the microphones and measurement environment, but procuring an ideal setup is usually difficult. To overcome this, we instead attempt to obtain an accurate HRTF using reinforcement learning. We performed simulations and verified that with the proposed method the HRTF accuracy improved on 24 horizontal directions. Also, in online learning experiments, the localization accuracy was improved for 3 subjects, suggesting the validity of our method.