Adaptive Modeling of HRTFs Based on Reinforcement Learning

Although recent studies on out-of-head sound localization technology have been aimed at applications in entertainment, this technology can also be used to provide an interface to connect a computer to the human brain. An effective out-of-head system requires an accurate head-related transfer function (HRTF). However, it is difficult to measure HRTF accurately. We propose a new method based on reinforcement learning to estimate HRTF accurately from measurement data and validate it through simulations. We used the actor-critic paradigm to learn the HRTF parameters and the autoregressive moving average (ARMA) model to reduce the number of such parameters. Our simulations suggest that an accurate HRTF can be estimated with this method. The proposed method is expected to be useful for not only entertainment applications but also brain-machine-interface (BMI) based on out-of-head sound localization technology.