Relaxation "sweet spot" exploration in pantophonic musical soundscape using reinforcement learning

Musical relaxation is a common method to relieve personal stress. Particularly, nature sounds, instrumental music, voice (chanting), "easy listening" songs, etc. can be played for relaxation. Nevertheless, effectiveness of the sounds used for the relaxation is idiosyncratic, depending on personal taste. In our approach, computer-guided audition for spatial soundscapes is investigated, automatically exploring a polyphonic area while using biosignals as indicators of satisfaction. We propose a reinforcement learning (RL) method to discover the sound relaxation "sweet spot" in a polyphonic soundscape. An avatar roams within a pantophonic space, surrounded by six independent audio channels, while a human subject, listening through the avatar's ears, is connected to an electroencephalographic (EEG) headset. Besides the position of the avatar, pitch, reverberation, and filters can also be changed to find the most relaxing virtual standpoint and parameters for the listener. Instead of changing position manually, a Deep Q-Network (DQN) in reinforcement learning is used. An RL agent adjusts parameters according to reward values calculated by change of relative theta band (4--8 Hz) power.