Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Mode

2026-08-14

AI Summary

This study addresses the ill-posed reconstruction challenge in high-order Ambisonics encoding for arbitrary microphone arrays by proposing a device-agnostic diffusion generative framework. Leveraging diffusion models with posterior sampling consistency constraints, this approach overcomes spatial detail reconstruction bottlenecks under limited measurements, enabling precise mapping from arbitrary sparse arrays to high-order sound fields. Experimental results demonstrate that the proposed framework stably estimates 12th-order Ambisonics signals and significantly outperforms existing linear and neural baselines in perceptual similarity. Consequently, this work provides a universal solution for high-fidelity spatial audio acquisition across heterogeneous array configurations, effectively bridging the gap between sparse sensing and comprehensive sound field representation through advanced generative modeling techniques.

Abstract

We address the problem of encoding room impulse responses (RIRs) into high-order Ambisonics (HOA) representations from arbitrary and potentially insufficient or incomplete microphone array measurements. This task is fundamentally ill-posed for microphone arrays with limited spatial capture capabilities, such as irregular or sparse arrays, as classical linear methods fail to reconstruct high-order spatial detail. We introduce a diffusion-based generative framework that models the statistical properties of HOA RIRs. This enables device-agnostic encoding from arbitrary microphone arrays, potentially unseen during data measurement. Our approach incorporates a posterior sampling procedure that enforces consistency between the estimated signals and the measurements while plausibly reconstructing spatial information that is unobservable from the limited measurements alone. Experiments on simulated data demonstrate that our method outperforms linear and neural baselines, achieving accurate HOA RIR estimation up to 12th order. A listening test with binaural renderings, including both simulated and measured RIRs, further confirms that the proposed method yields higher perceptual similarity to reference Ambisonics RIRs than all baselines. The flexibility and accuracy of the proposed framework opens new possibilities for scalable acoustics simulations.

Problem

• Room Impulse Responses
• High-order Ambisonics
• Sparse Microphone Arrays
• Ill-posed Inverse Problem
• Spatial Audio Encoding

Innovation

• Diffusion Model
• High-Order Ambisonics
• Device-Agnostic Encoding
• Posterior Sampling
• Room Impulse Response