ShanghaiTech University Knowledge Management System
| Shape and Material from Sound | |
| 2017 | |
| 会议录名称 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017)
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| 卷号 | 30 |
| 发表状态 | 已发表 |
| 摘要 | Hearing an object falling onto the ground, humans can recover rich information including its rough shape, material, and falling height. In this paper, we build machines to approximate such competency. We first mimic human knowledge of the physical world by building an efficient, physics-based simulation engine. Then, we present an analysis-by-synthesis approach to infer properties of the falling object. We further accelerate the process by learning a mapping from a sound wave to object properties, and using the predicted values to initialize the inference. This mapping can be viewed as an approximation of human commonsense learned from past experience. Our model performs well on both synthetic audio clips and real recordings without requiring any annotated data. We conduct behavior studies to compare human responses with ours on estimating object shape, material, and falling height from sound. Our model achieves near-human performance. |
| 会议地点 | Long Beach, CA, United states |
| 收录类别 | CPCI ; EI |
| 语种 | 英语 |
| 资助项目 | Center for Brain, Minds and Machines (NSF STC award)[CCF-1231216] |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000452649401031 |
| 出版者 | NEURAL INFORMATION PROCESSING SYSTEMS (NIPS) |
| EI入藏号 | 20182105215967 |
| EI主题词 | Mapping |
| EI分类号 | Surveying:405.3 ; Ergonomics and Human Factors Engineering:461.4 |
| 原始文献类型 | Proceedings Paper |
| 引用统计 | 正在获取...
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| 文献类型 | 会议论文 |
| 条目标识符 | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/20813 |
| 专题 | 信息科学与技术学院 信息科学与技术学院_本科生 |
| 通讯作者 | Zhang, Zhoutong |
| 作者单位 | 1.MIT, Cambridge, MA 02139 USA 2.Univ Cambridge, Cambridge, England 3.ShanghaiTech Univ, Shanghai, Peoples R China 4.Google Res, Mountain View, CA USA |
| 推荐引用方式 GB/T 7714 | Zhang, Zhoutong,Li, Qiujia,Huang, Zhengjia,et al. Shape and Material from Sound[C]:NEURAL INFORMATION PROCESSING SYSTEMS (NIPS),2017. |
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