Visually Informed Binaural Audio Generation without Binaural Audios
This addresses the limitation of scarce binaural datasets for immersive audio generation, offering a more scalable solution for real-world applications.
The paper tackles the problem of generating binaural audio without requiring binaural recordings by proposing PseudoBinaural, a pipeline that uses mono data and visual cues to create pseudo training pairs, achieving comparable performance to supervised methods in cross-dataset evaluations and subjective preferences.
Stereophonic audio, especially binaural audio, plays an essential role in immersive viewing environments. Recent research has explored generating visually guided stereophonic audios supervised by multi-channel audio collections. However, due to the requirement of professional recording devices, existing datasets are limited in scale and variety, which impedes the generalization of supervised methods in real-world scenarios. In this work, we propose PseudoBinaural, an effective pipeline that is free of binaural recordings. The key insight is to carefully build pseudo visual-stereo pairs with mono data for training. Specifically, we leverage spherical harmonic decomposition and head-related impulse response (HRIR) to identify the relationship between spatial locations and received binaural audios. Then in the visual modality, corresponding visual cues of the mono data are manually placed at sound source positions to form the pairs. Compared to fully-supervised paradigms, our binaural-recording-free pipeline shows great stability in cross-dataset evaluation and achieves comparable performance under subjective preference. Moreover, combined with binaural recordings, our method is able to further boost the performance of binaural audio generation under supervised settings.