Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Mode
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.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
This paper presents a significant advancement in spatial audio processing by effectively leveraging diffusion models to solve the ill-posed problem of high-order Ambisonics encoding from sparse measurements, achieving state-of-the-art perceptual and objective performance.
Methodology
The paper proposes a novel application of diffusion models to the ill-posed inverse problem of encoding Room Impulse Responses (RIRs) into High-Order Ambisonics (HOA) from sparse or irregular microphone arrays. The core methodological contribution is the integration of a device-agnostic diffusion prior with a posterior sampling procedure that enforces data consistency via a range-projected likelihood term. The use of a hybrid time-frequency/time-domain architecture (NCSN++ backbone with a U-Net refinement stage for early reflections) is a well-reasoned design choice given the distinct temporal characteristics of RIRs. The introduction of a compressed spectrogram distance metric for the likelihood guidance is a technically sound innovation to emphasize weak high-order components. The formulation separates the device-agnostic prior from the device-specific likelihood, which is a strong theoretical foundation for generalization.
Results & evaluation
The experimental evaluation is rigorous and comprehensive. The authors utilize two datasets: a large-scale internal FDTD simulation dataset and the public Treble-10 dataset. They compare against three strong baselines: Linear Least-Squares, a Time-Dependent Neural Encoder, and a Conditional Diffusion model specific to the device. The inclusion of both objective metrics (EDC, NPM) and a subjective listening test (MUSHRA-style with binaural rendering) provides robust validation. The results demonstrate clear superiority in both perceptual similarity and objective metrics, particularly in preserving early reflections and late reverberation tails. The ablation studies effectively isolate the contribution of the range-projection constraint and the hybrid architecture.
Reproducibility
The paper provides sufficient detail for reproduction, including dataset descriptions, model architectures (NCSN++ based), training hyperparameters (AdamW, learning rate, batch size), and specific implementation details like the compressed spectrogram definition. The use of standard libraries (PyTorch) and well-known architectures aids reproducibility. However, the reliance on an internal FDTD dataset for the primary training results limits independent verification of the scale of the results, though the public Treble-10 results offer some ground truth.
Limitations
The primary limitation is the distribution gap between simulated training data and real-world measured RIRs, which the authors acknowledge and demonstrate leads to performance drops on measured data (Eigenmike-64). The method assumes a highly accurate Array Transfer Function (ATF), which may not hold in real-world deployments with calibration errors. Additionally, the iterative sampling process is computationally intensive, making it less suitable for real-time applications without significant acceleration techniques. The evaluation is currently limited to a single device configuration (Aria Glasses) for the device-agnostic claim, though the method is theoretically general.
Broader impact
This work has significant implications for spatial audio processing, particularly for Virtual Reality (VR) and Augmented Reality (AR) applications where scalable and accurate acoustic simulation is crucial. By enabling high-quality HOA encoding from sparse arrays, it lowers the barrier for capturing spatial audio with wearable devices. It also facilitates the generation of large-scale training data for spatial audio models. The device-agnostic nature of the approach promotes interoperability across different hardware platforms. This paper presents a significant advancement in spatial audio processing by effectively leveraging diffusion models to solve the ill-posed problem of high-order Ambisonics encoding from sparse measurements, achieving state-of-the-art perceptual and objective performance.
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