2017/12/04 by Yipin Zhou, Zhaowen Wang, Zhou, Yipin +8 · 1 voice · 15 citations
Computer Science · Engineering · Psychology · #Acoustics #Artificial intelligence #Computer science #Computer vision #Engineering #Human–computer interaction #Modalities #Music and Audio Processing #Natural (archaeology) #Natural sounds #Perception #Psychology #Sight #Sound (geography) #Speech and Audio Processing #Speech recognition #Task (project management) #Variety (cybernetics) #Video Analysis and Summarization #cs.CV
paper · pdf · doi:10.48550/arxiv.1712.01393
published in arXiv (Cornell University) (Cornell University) · Project page: http://bvision11.cs.unc.edu/bigpen/yipin/visual2sound_webpage/visual2sound.html
openalex publication_date 2017/12/04 · arxiv created 2018/06/01 · arxiv updated 2018/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlated during natural events, these two modalities combine to jointly affect human perception. In this paper, we pose the task of generating sound given visual input. Such capabilities could help enable applications in virtual reality (generating sound for virtual scenes automatically) or provide additional accessibility to images or videos for people with visual impairments. As a first step in this direction, we apply learning-based methods to generate raw waveform samples given input video frames. We evaluate our models on a dataset of videos containing a variety of sounds (such as ambient sounds and sounds from people/animals). Our experiments show that the generated sounds are fairly realistic and have good temporal synchronization with the visual inputs.