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Shape and Material from Sound
Neural Information Processing Systems
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 objectdblp:conf/nips/zhangLH0TF17 fatcat:wqt74kyhcjb6rlfvhecjjnzhla