# Scene-Aware Audio Rendering via Deep Acoustic Analysis

arXiv: https://arxiv.org/abs/1911.06245 · HTML: https://mlsanity.com/paper/1911.06245/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [AV-NeRF: Learning Neural Fields for Real-World Audio-Visual Scene Synthesis](https://mlsanity.com/paper/2302.02088/) (2023)
- [Neural Acoustic Context Field: Rendering Realistic Room Impulse Response With Neural Fields](https://mlsanity.com/paper/2309.15977/) (2023)
- [Geometry-Aware Multi-Task Learning for Binaural Audio Generation from Video](https://mlsanity.com/paper/2111.10882/) (2021)
- [AV-RIR: Audio-Visual Room Impulse Response Estimation](https://mlsanity.com/paper/2312.00834/) (2023)
- [Listen2Scene: Interactive material-aware binaural sound propagation for reconstructed 3D scenes](https://mlsanity.com/paper/2302.02809/) (2023)
- [Novel-View Acoustic Synthesis from 3D Reconstructed Rooms](https://mlsanity.com/paper/2310.15130/) (2023)
- [How Would It Sound? Material-Controlled Multimodal Acoustic Profile Generation for Indoor Scenes](https://mlsanity.com/paper/2508.02905/) (2025)
- [Exploratory Evaluation of Speech Content Masking](https://mlsanity.com/paper/2401.03936/) (2024)
- [Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction](https://mlsanity.com/paper/2110.02405/) (2021)
- [Explicit Context-Driven Neural Acoustic Modeling for High-Fidelity RIR Generation](https://mlsanity.com/paper/2509.15210/) (2025)

## Abstract (arXiv)

We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sources with virtual models. Given the captured audio and an approximate geometric model of a real-world room, we present a novel learning-based method to estimate its acoustic material properties. Our approach is based on deep neural networks that estimate the reverberation time and equalization of the room from recorded audio. These estimates are used to compute material properties related to room reverberation using a novel material optimization objective. We use the estimated acoustic material characteristics for audio rendering using interactive geometric sound propagation and highlight the performance on many real-world scenarios. We also perform a user study to evaluate the perceptual similarity between the recorded sounds and our rendered audio.