Binaural source localization using deep learning and head rotation information

This work studies learning-based binaural sound source localization, under the influence of head rotation in reverberant conditions. Emphasis is on whether knowledge of head rotation can improve localization performance over the non-rotating case for the same acoustic scene. Simulations of binaural head signals of a static and rotating head were conducted, for 5 different rotation speeds and a wide range of reverberant conditions. Several convolutional recurrent neural network models were evaluated including a static head scenario, a model without rotation information, and distinct models differentiated on the way of manipulating the quaternions. The results were analyzed based on the direction-of-arrival error, and they show the importance of using quaternions as additional features, with the best localization accuracy obtained when using an additional convolutional branch that merges the features through addition or concatenation. Nevertheless, raw quaternion features presented lower performance than the static baseline model. Additionally, the study shows the importance of the analysis time window length when using information about head rotation.