BAST-Mamba: Binaural Audio Spectrogram Mamba Transformer for binaural sound localization
Journal: Neurocomputing
Year: 2025

Accurate sound localization in reverberant environments is essential for human auditory perception. Recently, Convolutional Neural Networks (CNNs) have been used to model the binaural human auditory pathway. However, CNNs face limitations in capturing global acoustic features. To address this issue, we propose a novel end-to-end Binaural Audio Spectrogram Mamba Transformer (BAST-Mamba) model to predict sound azimuth in both anechoic and reverberant conditions. We explore two implementation modes