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arXiv · 2501.02786

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

Yuanhong Chen·Kazuki Shimada·Christian Simon·Yukara Ikemiya·Takashi Shibuya·Yuki Mitsufuji
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Abstract

Binaural audio generation (BAG) aims to convert monaural audio to stereo audio using visual prompts, requiring a deep understanding of spatial and semantic information. However, current models risk overfitting to room environments and lose fine-grained spatial details. In this paper, we propose a new audio-visual binaural generation model incorporating an audio-visual conditional normalisation layer that dynamically aligns the mean and variance of the target difference audio features using visual context, along with a new contrastive learning method to enhance spatial sensitivity by mining negative samples 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 and MUSIC-Stereo benchmarks.

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Yuanhong Chen, Kazuki Shimada, Christian Simon, Yukara Ikemiya, Takashi Shibuya, Yuki Mitsufuji. 2025-01-06. CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation. https://doi.org/10.1145/3746027.3754919

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