Materialistic RIR: Material Conditioned Realistic RIR Generation

2026-04-22

AI Summary

This work addresses the challenge that existing acoustic modeling approaches struggle to disentangle the effects of spatial geometry and material properties on room impulse responses (RIRs), thereby limiting user controllability and perceptual realism. To overcome this, the authors propose a dual-module neural network architecture that explicitly separates geometric and material factors: a spatial module models room geometry, while a material module modulates the acoustic response according to user-specified material configurations. This approach achieves, for the first time, explicit disentanglement of geometry and materials in RIR generation, significantly enhancing both controllability and realism. Experiments demonstrate consistent improvements over baseline methods, with a 16% gain in acoustic fidelity (measured by RTE) and a 70% improvement in material-related metrics; subjective listening tests further confirm higher perceived realism and greater sensitivity to material variations.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Room Impulse Response
• material disentanglement
• acoustic modeling
• spatial layout
• material conditioning

Innovation

• material-conditioned
• disentangled RIR generation
• spatial-material disentanglement
• realistic acoustics
• Room Impulse Response