分离空间与材质对声音的影响,实现可调控的逼真混响生成。
Materialistic RIR: Material Conditioned Realistic RIR Generation

- 分两模块建模:空间结构+材质配置,解耦声学影响。
- 在声学与材质指标上分别提升16%和70%。
- 适合虚拟现实、建筑设计等需要精准声效控制的场景。
我们听到的声音不仅取决于环境的空间布局,还受其中物体和表面材质影响。例如,相同布局的房间,木墙与混凝土墙会产生截然不同的听觉体验。准确建模这一效应对虚拟现实、机器人、建筑与音频工程至关重要。然而现有方法常将空间与材质影响耦合,限制了用户控制力并降低真实性。本文提出一种新型材料可控的混响响应(RIR)生成方法,通过两个模块显式解耦空间与材质作用:空间模块捕捉布局影响,材质模块根据用户指定的材质配置调制该空间混响。此设计允许用户仅更改材质即可观察其对声音的影响,而不改变空间结构或内容。模型在声学指标(最高提升16%于RTE)和材质相关指标(最高提升70%)上显著优于先前方法。人耳感知实验进一步证明其更真实的音效与更强的材质敏感性。
原文摘要 · Abstract (English)
Rings like gold, thuds like wood! The sound we hear in a scene is shaped not only by the spatial layout of the environment but also by the materials of the objects and surfaces within it. For instance, a room with wooden walls will produce a different acoustic experience from a room with the same spatial layout but concrete walls. Accurately modeling these effects is essential for applications such as virtual reality, robotics, architectural design, and audio engineering. Yet, existing methods for acoustic modeling often entangle spatial and material influences in correlated representations, which limits user control and reduces the realism of the generated acoustics. In this work, we present a novel approach for material-controlled Room Impulse Response (RIR) generation that explicitly disentangles the effects of spatial and material cues in a scene. Our approach models the RIR using two modules: a spatial module that captures the influence of the spatial layout of the scene, and a material module that modulates this spatial RIR according to a user-specified material configuration. This explicitly disentangled design allows users to easily modify the material configuration of a scene and observe its impact on acoustics without altering the spatial structure or scene content. Our model provides significant improvements over prior approaches on both acoustic-based metrics (up to +16% on RTE) and material-based metrics (up to +70%). Furthermore, through a human perceptual study, we demonstrate the improved realism and material sensitivity of our model compared to the strongest baselines.
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