  Accurate sound localization in a reverberation environment is essential for
human auditory perception. Recently, Convolutional Neural Networks (CNNs) have
been utilized to model the binaural human auditory pathway. However, CNN shows
barriers in capturing the global acoustic features. To address this issue, we
propose a novel end-to-end Binaural Audio Spectrogram Transformer (BAST) model
to predict the sound azimuth in both anechoic and reverberation environments.
Two modes of implementation, i.e. BAST-SP and BAST-NSP corresponding to BAST
model with shared and non-shared parameters respectively, are explored. Our
model with subtraction interaural integration and hybrid loss achieves an
angular distance of 1.29 degrees and a Mean Square Error of 1e-3 at all
azimuths, significantly surpassing CNN based model. The exploratory analysis of
the BAST's performance on the left-right hemifields and anechoic and
reverberation environments shows its generalization ability as well as the
feasibility of binaural Transformers in sound localization. Furthermore, the
analysis of the attention maps is provided to give additional insights on the
interpretation of the localization process in a natural reverberant
environment.
