Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation

2025-09-18

AI Summary

Existing neural implicit room impulse response (RIR) modeling approaches neglect explicit geometric priors, limiting acoustic prediction accuracy. To address this, we propose Mesh-infused Neural Acoustic Field (MiNAF), the first method to embed local distance distributions derived from coarse room meshes as explicit geometric features into a neural implicit field—enabling joint image- and geometry-aware acoustic modeling. MiNAF jointly encodes scene images and 3D distance features to guide a deep network in generating context-aware RIRs. Evaluated on multiple benchmarks, MiNAF significantly outperforms state-of-the-art methods, particularly under few-shot settings, where it demonstrates superior robustness and high-fidelity RIR reconstruction. By unifying visual and geometric cues within a neural implicit framework, MiNAF establishes a new paradigm for data-efficient, geometry-informed neural acoustic modeling.

Technology Category

• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Machine Learning: Learning with Manifolds
• Computer Vision: 3D Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Realistic sound simulation plays a critical role in many applications. A key element in sound simulation is the room impulse response (RIR), which characterizes how sound propagates from a source to a listener within a given space. Recent studies have applied neural implicit methods to learn RIR using context information collected from the environment, such as scene images. However, these approaches do not effectively leverage explicit geometric information from the environment. To further exploit the potential of neural implicit models with direct geometric features, we present Mesh-infused Neural Acoustic Field (MiNAF), which queries a rough room mesh at given locations and extracts distance distributions as an explicit representation of local context. Our approach demonstrates that incorporating explicit local geometric features can better guide the neural network in generating more accurate RIR predictions. Through comparisons with conventional and state-of-the-art baseline methods, we show that MiNAF performs competitively across various evaluation metrics. Furthermore, we verify the robustness of MiNAF in datasets with limited training samples, demonstrating an advance in high-fidelity sound simulation.

Problem

• Generating high-fidelity room impulse responses using neural networks
• Leveraging explicit geometric information for improved acoustic modeling
• Enhancing sound simulation accuracy with local context features

Innovation

• Uses explicit geometric features from room mesh
• Extracts distance distributions as local context
• Guides neural network for accurate RIR generation