# Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields

arXiv: https://arxiv.org/abs/2309.15977 · HTML: https://mlsanity.com/paper/2309.15977/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 27 Sep 2023.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021)
- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [FAST-RIR: Fast neural diffuse room impulse response generator](https://mlsanity.com/paper/2110.04057/) (2021)
- [Scene-Aware Audio for 360textdegree Videos](https://mlsanity.com/paper/1805.04792/) (2018)
- [GWA: A Large High-Quality Acoustic Dataset for Audio Processing](https://mlsanity.com/paper/2204.01787/) (2022)
- [Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks](https://mlsanity.com/paper/2202.03416/) (2022)
- [Scene-Aware Audio Rendering via Deep Acoustic Analysis](https://mlsanity.com/paper/1911.06245/) (2019)
- [Gridless 3D Recovery of Image Sources from Room Impulse Responses](https://mlsanity.com/paper/2208.14017/) (2022)

## Built on by (most cited first, 23 of 23)

- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark](https://mlsanity.com/paper/2403.18821/) (2024)
- [Language-Guided Joint Audio-Visual Editing via One-Shot Adaptation](https://mlsanity.com/paper/2410.07463/) (2024)
- [NeRAF: 3D Scene Infused Neural Radiance and Acoustic Fields](https://mlsanity.com/paper/2405.18213/) (2024)
- [Hearing Anywhere in Any Environment](https://mlsanity.com/paper/2504.10746/) (2025)
- [Differentiable Room Acoustic Rendering with Multi-View Vision Priors](https://mlsanity.com/paper/2504.21847/) (2025)
- [Learning to Highlight Audio by Watching Movies](https://mlsanity.com/paper/2505.12154/) (2025)
- [BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models](https://mlsanity.com/paper/2505.22865/) (2025)
- [ZeroSep: Separate Anything in Audio with Zero Training](https://mlsanity.com/paper/2505.23625/) (2025)
- [How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes](https://mlsanity.com/paper/2508.02905/) (2025)
- [SOAF: Scene Occlusion-aware Neural Acoustic Field](https://mlsanity.com/paper/2407.02264/) (2024)
- [SoundVista: Novel-View Ambient Sound Synthesis via Visual-Acoustic Binding](https://mlsanity.com/paper/2504.05576/) (2025)
- [ActiveRIR: Active Audio-Visual Exploration for Acoustic Environment Modeling](https://mlsanity.com/paper/2404.16216/) (2024)
- [Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives](https://mlsanity.com/paper/2502.11858/) (2025)
- [Can NeRFs See without Cameras?](https://mlsanity.com/paper/2505.22441/) (2025)
- [Few-shot Acoustic Synthesis with Multimodal Flow Matching](https://mlsanity.com/paper/2603.19176/) (2026)
- [Direction-Aware Neural Acoustic Fields for Few-Shot Interpolation of Ambisonic Impulse Responses](https://mlsanity.com/paper/2505.13617/) (2025)
- [Resounding Acoustic Fields with Reciprocity](https://mlsanity.com/paper/2510.20602/) (2025)
- [MAGIC: Map-Guided Few-Shot Audio-Visual Acoustics Modeling](https://mlsanity.com/paper/2405.13860/) (2024)
- [Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation](https://mlsanity.com/paper/2509.15210/) (2025)
- [Materialistic RIR: Material Conditioned Realistic RIR Generation](https://mlsanity.com/paper/2604.21119/) (2026)
- [PathRIR: Physics-Guided Acoustic Path Selection and Late-Tail Compensation for Fast Room Impulse Response Simulation](https://mlsanity.com/paper/2607.23293/) (2026)
- [Audible World Models: Spatially Aware Sound Generation for 3D Worlds](https://mlsanity.com/paper/2609.38444/) (2026)

## Abstract (arXiv)

Room impulse response (RIR), which measures the sound propagation within an environment, is critical for synthesizing high-fidelity audio for a given environment. Some prior work has proposed representing RIR as a neural field function of the sound emitter and receiver positions. However, these methods do not sufficiently consider the acoustic properties of an audio scene, leading to unsatisfactory performance. This letter proposes a novel Neural Acoustic Context Field approach, called NACF, to parameterize an audio scene by leveraging multiple acoustic contexts, such as geometry, material property, and spatial information. Driven by the unique properties of RIR, i.e., temporal un-smoothness and monotonic energy attenuation, we design a temporal correlation module and multi-scale energy decay criterion. Experimental results show that NACF outperforms existing field-based methods by a notable margin. Please visit our project page for more qualitative results.