Lightweight Scene-aware Rain Sound Simulation for Interactive Virtual Environments

Haonan Cheng, Shiguang Liu, Jiawan Zhang
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Abstract

We present a lightweight and efficient rain sound synthesis method for interactive virtual environments. Existing rain sound simulation methods require massive superposition of scene-specific precomputed rain sounds, which is excessive memory consumption for virtual reality systems (e.g. video games) with limited audio memory budgets. Facing this issue, we reduce the audio memory budgets by introducing a lightweight rain sound synthesis method which is only based on eight physically-inspired basic rain sounds. First, in order to generate sufficiently various rain sounds with limited sound data, we propose an exponential moving average based frequency domain additive (FDA) synthesis method to extend and modify the pre-computed basic rain sounds. Each rain sound is generated in the frequency domain before conversion back to the time domain, allowing us to extend the rain sound which is free of temporal distortions and discontinuities. Next, we introduce an efficient binaural rendering method to simulate the 3D perception that coheres with the visual scene based on a set of Near-Field Transfer Functions (NFTF). Various results demonstrate that the proposed method drastically decreases the memory cost (77 times compressed) and overcomes the limitations of existing methods in terms of interaction.
用于交互式虚拟环境的轻量级场景感知雨声模拟
提出了一种适用于交互式虚拟环境的轻量级高效雨声合成方法。现有的雨声模拟方法需要大量叠加场景特定的预先计算的雨声,这对于音频内存预算有限的虚拟现实系统(例如视频游戏)来说是过度的内存消耗。面对这个问题,我们通过引入一种轻量级的雨声合成方法来减少音频内存预算,这种方法仅基于八个物理启发的基本雨声。首先,为了在有限的声音数据下产生足够多样的雨声,我们提出了一种基于指数移动平均的频域加性(FDA)合成方法来扩展和修改预先计算的基本雨声。在转换回时域之前,每个雨声都是在频域中产生的,这使我们能够扩展雨声,使其不受时间扭曲和不连续的影响。接下来,我们引入了一种高效的双耳渲染方法,基于一组近场传递函数(NFTF)来模拟与视觉场景一致的3D感知。各种结果表明,所提出的方法大大降低了内存成本(压缩77倍),克服了现有方法在交互方面的局限性。
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