Mean absorption estimation from room impulse responses using virtually supervised learning

TL;DR

This paper presents a novel neural network-based method to estimate mean absorption coefficients from room impulse responses, bypassing traditional geometric measurements, and demonstrates its effectiveness on simulated and real data.

Contribution

It introduces a virtually-supervised learning approach for absorption estimation directly from RIRs, improving over classical formulas especially when reverberation times are unreliable.

Findings

Neural models perform comparably to classical methods at 1 kHz and above.

The approach works well even when reverberation times are difficult to estimate.

Extensive simulations and real-world tests validate the method's robustness.

Abstract

In the context of building acoustics and the acoustic diagnosis of an existing room, this paper introduces and investigates a new approach to estimate mean absorption coefficients solely from a room impulse response (RIR). This inverse problem is tackled via virtually-supervised learning, namely, the RIR-to-absorption mapping is implicitly learned by regression on a simulated dataset using artificial neural networks. We focus on simple models based on well-understood architectures. The critical choices of geometric, acoustic and simulation parameters used to train the models are extensively discussed and studied, while keeping in mind conditions that are representative of the field of building acoustics. Estimation errors from the learned neural models are compared to those obtained with classical formulas that require knowledge of the room's geometry and reverberation times. Extensive comparisons made on a variety of simulated test sets highlight different conditions under which the learned models can overcome the well-known limitations of the diffuse sound field hypothesis underlying these formulas. Results obtained on real RIRs measured in an acoustically configurable room show that at 1~kHz and above, the proposed approach performs comparably to classical models when reverberation times can be reliably estimated, and continues to work even when they cannot.