CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation

摘要

Binaural audio generation (BAG) aims to convert monaural audio to stereo audio using visual prompts, requiring a deep understanding of spatial and semantic information. The success of the BAG systems depends on the effectiveness of cross-modal reasoning and spatial understanding. Current methods have explored the use of visual information as guidance for binaural audio generation. However, they rely solely on cross-attention mechanisms to guide the generation process and under-utilise the temporal and spatial information in video data during training and inference. These limitations result in the loss of fine-grained spatial details and risk overfitting to specific environments, ultimately constraining model performance. In this paper, we address the aforementioned issues by introducing a new audio-visual binaural generation model with an audio-visual conditional normalisation layer that dynamically aligns the target difference audio features using visual context. To enhance spatial sensitivity, we also introduce a contrastive learning method that mines negatives from shuffled visual features. We also introduce a cost-efficient way to utilise test-time augmentation in video data to enhance performance. Our approach achieves state-of-the-art generation accuracy on the FAIR-Play, MUSIC-Stereo, and YT-MUSIC benchmarks. Code is available at https://github.com/SonyResearch/CCStereo.