Accurate sound source localization and novel-view acoustic synthesis have become increasingly important in various applications, including virtual and augmented reality, film and game production, and acoustic consulting. However, the complex relationships between sound waves and their physical environment make it challenging to estimate the sound in a scene. This article explores the benefits of combining blind audio recordings with 3D scene information to achieve novel-view acoustic synthesis.
Novel-View Acoustic Synthesis
We investigate the benefit of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. Given audio recordings from 2-4 microphones and the 3D geometry and material of a scene containing multiple unknown sound sources, we estimate the sound anywhere in the scene. We identify the main challenges of novel-view acoustic synthesis as sound source localization, separation, and dereverberation.
Naively training an end-to-end network fails to produce high-quality results. However, we show that incorporating room impulse responses (RIRs) derived from 3D reconstructed rooms enables the same network to jointly tackle these tasks. Our method outperforms existing methods designed for the individual tasks, demonstrating its effectiveness at utilizing 3D visual information.
Results
In a simulated study on the Matterport3D-NVAS dataset, our model achieves near-perfect accuracy on source localization, a PSNR of 26.44dB and a SDR of 14.23dB for source separation and dereverberation, resulting in a PSNR of 25.55 dB and a SDR of 14.20 dB on novel-view acoustic synthesis.
Conclusion
Our method demonstrates the effectiveness of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis. We release our code and model on our project website at https://github.com/apple/ml-nvas3d. Please wear headphones when listening to the results.
Frequently Asked Questions
Q1: What is novel-view acoustic synthesis?
Novel-view acoustic synthesis is the process of estimating the sound in a scene based on audio recordings and 3D scene information, allowing for the creation of new audio experiences that match the original scene.
Q2: How does your method overcome the challenges of sound source localization, separation, and dereverberation?
Our method incorporates room impulse responses (RIRs) derived from 3D reconstructed rooms, enabling the network to jointly tackle these tasks and outperform existing methods designed for individual tasks.
Q3: What are the main challenges of novel-view acoustic synthesis?
The main challenges of novel-view acoustic synthesis are sound source localization, separation, and dereverberation, which require the estimation of the sound anywhere in the scene.
Q4: How do you estimate the sound in a scene?
We estimate the sound in a scene by combining blind audio recordings with 3D scene information, allowing us to utilize the physical properties of the scene to infer the sound.
Q5: Why should I use your method?
Our method demonstrates the effectiveness of combining blind audio recordings with 3D scene information for novel-view acoustic synthesis, providing a powerful tool for creating new audio experiences that match the original scene.
