Details

Autor(en) / Beteiligte

• Lu, Yu-Ding
• Lee, Hsin-Ying
• Tseng, Hung-Yu
• Yang, Ming-Hsuan

Titel

Self-Supervised Audio Spatialization with Correspondence Classifier

Ist Teil von

• 2019 IEEE International Conference on Image Processing (ICIP), 2019, p.3347-3351

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore (IEEE/IET Electronic Library - IEL)

Beschreibungen/Notizen

• Spatial audio is an essential medium to audiences for 3D visual and auditory experience. However, the recording devices and techniques are expensive or inaccessible to the general public. In this work, we propose a self-supervised audio spatialization network that can generate spatial audio given the corresponding video and monaural audio. To enhance spatialization performance, we use an auxiliary classifier to classify ground-truth videos and those with audio where the left and right channels are swapped. We collect a large-scale video dataset with spatial audio to validate the proposed method. Experimental results demonstrate the effectiveness of the proposed model on the audio spatialization task.