Hearing Anything Anywhere (2406.07532v1)

Abstract: Recent years have seen immense progress in 3D computer vision and computer graphics, with emerging tools that can virtualize real-world 3D environments for numerous Mixed Reality (XR) applications. However, alongside immersive visual experiences, immersive auditory experiences are equally vital to our holistic perception of an environment. In this paper, we aim to reconstruct the spatial acoustic characteristics of an arbitrary environment given only a sparse set of (roughly 12) room impulse response (RIR) recordings and a planar reconstruction of the scene, a setup that is easily achievable by ordinary users. To this end, we introduce DiffRIR, a differentiable RIR rendering framework with interpretable parametric models of salient acoustic features of the scene, including sound source directivity and surface reflectivity. This allows us to synthesize novel auditory experiences through the space with any source audio. To evaluate our method, we collect a dataset of RIR recordings and music in four diverse, real environments. We show that our model outperforms state-ofthe-art baselines on rendering monaural and binaural RIRs and music at unseen locations, and learns physically interpretable parameters characterizing acoustic properties of the sound source and surfaces in the scene.

• The paper introduces DiffRIR, a novel framework that reconstructs spatial acoustics using approximately 12 RIR samples and planar geometric models.
• It employs source localization, reflection path tracing, and Fourier inversions to estimate both direct and reflected sound contributions.
• Experiments demonstrate DiffRIR’s superior performance and robustness in diverse real-world environments, with improved accuracy over prior methods.

Hearing Anything Anywhere: An Expert Analysis

The paper "Hearing Anything Anywhere" presented by Mason Long Wang et al. introduces DiffRIR, a novel framework for spatial acoustic reconstruction in real-world environments. The primary objective is to synthesize accurate and immersive auditory experiences utilizing a sparse set of room impulse response (RIR) measurements (approximately 12) and a planar geometric reconstruction of the environment. This setup is designed to be practical and achievable for ordinary users, paving the way for applications in mixed reality (XR) and virtual reality (VR).

Problem Definition and Challenges

The key challenge addressed is the reconstruction of spatial acoustic characteristics with significantly fewer RIR measurements as opposed to traditional methods that may require hundreds of measurements. The paper contrasts visual and auditory signal characteristics, highlighting that while visual scenes emit light that travels instantly, audio signals are time-varying and complex due to their slower propagation speed and interactions with environmental surfaces resulting in reflections and reverberations. The problem is formulated as inferring RIRs at unobserved listener locations from a small set of measured RIRs, analogous to sparse-view novel view synthesis (NVS) in computer vision.

The DiffRIR Framework

The DiffRIR framework involves several critical components:

1. Source Localization and Modeling: The initial step involves estimating the sound source location using time-of-arrival methods. The framework models the source’s directivity and impulse response, capturing frequency-dependent directional radiation characteristics.
2. Reflection Path Tracing: Utilizing an image-source method, DiffRIR computes specular reflection paths up to a predetermined order. The framework models the acoustic effects of each surface reflection, incorporating frequency-dependent reflectivity parameters.
3. Combination of Path Contributions: Each reflection path's contribution is determined through a minimum-phase inverse Fourier transform. The aggregated contributions of multiple reflection paths are combined with a residual model accounting for diffuse reflections and other higher-order effects to produce the final RIR estimate.

Validation and Dataset

The researchers validate their method using a novel dataset containing RIR measurements from four diverse real-world environments, each varying in material, shape, and complexity. Environments include a standard classroom, a semi-anechoic chamber, a reverberant hallway, and a complexly shaped room. Evaluations include predicting monaural and binaural RIRs and music at unseen locations, achieving superior performance compared to state-of-the-art methods like DeepIR, NAF, and INRAS.

Technical Results

The experimental results underscore the efficacy of DiffRIR:

• Quantitative Metrics: The framework demonstrates lower Multiscale Log-Spectral L1 and Envelope distance errors across various room configurations.
• Robustness: DiffRIR exhibits robustness to geometric distortions and performs well even with a significantly reduced number of training points.
• Interpretability: The framework learns interpretable parameters such as source directivity patterns and surface reflection characteristics, enabling virtual modifications like rotating and translating the sound source, or simulating the effects of inserting reflective panels.

Practical and Theoretical Implications

From a practical standpoint, DiffRIR can significantly enhance XR applications by enabling immersive audio rendering in virtual environments without requiring extensive measurement setups. The framework's ability to synthesize physically plausible auditory scenes has implications for virtual reality, gaming, remote conferencing, and architectural design. Theoretically, it advances the understanding of inverse acoustic rendering by combining differentiable programming with geometric and physical modeling principles.

Future Directions

Future research could expand upon this work by:

• Multi-Source Environments: Extending the framework to environments with multiple, potentially moving, sound sources.
• Dynamic Scene Geometry: Adapting methods to accommodate dynamic and deformable environments.
• Data-Driven Approaches: Leveraging natural audio recordings to obviate the need for explicit RIR measurements.
• Enhanced Diffuse Sound Modeling: Improving the residual model to capture complex acoustic phenomena more comprehensively.

Overall, "Hearing Anything Anywhere" contributes valuable insights and methodological advancements to the field of 3D audio rendering, offering a scalable and efficient solution for capturing and synthesizing spatial acoustics in real-world environments.