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Binaural audio gives the listener an immersive experience and can enhanceaugmented and virtual reality. However, recording binaural audio requiresspecialized setup with a dummy human head having microphones in left and rightears. Such a recording setup is difficult to build and setup, therefore monoaudio has become the preferred choice in common devices. To obtain the sameimpact as binaural audio, recent efforts have been directed towards liftingmono audio to binaural audio conditioned on the visual input from the scene.Such approaches have not used an important cue for the task: the distance ofdifferent sound producing objects from the microphones. In this work, we arguethat depth map of the scene can act as a proxy for inducing distanceinformation of different objects in the scene, for the task of audiobinauralization. We propose a novel encoder-decoder architecture with ahierarchical attention mechanism to encode image, depth and audio featurejointly. We design the network on top of state-of-the-art transformer networksfor image and depth representation. We show empirically that the proposedmethod outperforms state-of-the-art methods comfortably for two challengingpublic datasets FAIR-Play and MUSIC-Stereo. We also demonstrate withqualitative results that the method is able to focus on the right informationrequired for the task. The project details are available at\url{https://krantiparida.github.io/projects/bmonobinaural.html}

Beyond Mono to Binaural: Generating Binaural Audio from Mono Audio with Depth and Cross Modal Attention