Physically-based Statistical Simulation of Rain Sound

A typical rainfall scenario contains tens of thousands of dynamic sound sources. A characteristic of the large-scale scene is the strong randomness in raindrop distribution, which makes it notoriously expensive to synthesize such sounds with purely physical methods. Moreover, the raindrops hitting different surfaces (liquid or various solids) can emit distinct sounds, for which prior methods with unified impact sound models are ill-suited. In this paper, we present a physically-based statistical simulation method to synthesize realistic rain sound, which respects surface materials. We first model the raindrop sound with two mechanisms, namely the initial impact and the subsequent pulsation of entrained bubbles. Then we generate material sound textures (MSTs) based on a specially designed signal decomposition and reconstruction model. This allows us to distinguish liquid surface with bubble sound and different solid surfaces with MSTs. Furthermore, we build a basic rain sound (BR-sound) bank with the proposed raindrop sound clustering method based on a statistical model, and design a sound source activator for simulating spatial propagation in an efficient manner. This novel method drastically decreases the computational cost while producing convincing sound results. Various experiments demonstrate the effectiveness of our sound simulation model.