Paper:
Shape and Material from Sound
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.

Review:
This paper contains a lot of ideas and methods packed into a tightly wound package - less would have been certainly more: The general aim is to build a system that can mimick the human ability to recognise shape and material objects from their sounds. Certainly a nice idea to explore within a NIPS community. To this end a generative model is defined, and a process for inference is proposed ("Physics-Based Audio Engine") that uses a physics simulation, this physics simulation is coupled (somehow) to a sound generation algorithm. This appears to use (somehow) the physics simulation engine representation of the vibrating surfaces of the objects to render the sounds. This engine is then used to create training stimuli for 4 variants of machine learning algorithms to learn appropriate representations. Again the descriptions are rather curt, so it is difficult to understand how the algorithms actually operate and how they perform. Into the paper mix we also have now human experiments that aims to compare the algorithms with that of the human observer. A description of how the experiment was conducted with human users is missing, however human users are almost always outperformed by the model. While it is up to this point unclear why the authors did not use real data when assessing their models, we are treated to real at the end (Figure 7) but without any meaningful information for us to understand how their method performs. In my opinion these are the major issues
1. Too much information therefore too superficially presented.
2. It is unclear how good the generative model is, comparison to real data would have been help here in anchoring it first.
3. The use of 4 different algorithms for classification is a nice effort, but it remains unclear to me what the point is (do the authors have a clear hypothesis?)
4. The human data is in itself interesting, but at this point it appears unclear how it was obtained, and how it informs us further on the work done in the paper.