Audio-Material Reconstruction for Virtualized Reality using a Probabilistic Damping Model

Modal sound synthesis has been used to create realistic sounds from rigid-body objects, but requires accurate real-world material parameters. These material parameters can be estimated from recorded sounds of an impacted object, but external factors can interfere with accurate parameter estimation. We present a novel technique for estimating the damping parameters of materials from recorded impact sounds that probabilistically models many of these external factors. We use a generative model to represent the combined effects of material damping, support damping, and sampling inaccuracies, then use maximum likelihood estimation to fit a damping model to recorded data. This technique simplifies the recording process, only requires audio as input without knowing the precise object geometry or the exact hit location, and uses multiple recorded impact sounds to improve accuracy. We validate the effectiveness of this technique with a comprehensive analysis of a synthetic dataset and a perceptual study on material identification.