Mean absorption estimation from room impulse responses using virtually
supervised learning
- URL: http://arxiv.org/abs/2109.00393v1
- Date: Wed, 1 Sep 2021 14:06:20 GMT
- Title: Mean absorption estimation from room impulse responses using virtually
supervised learning
- Authors: C\'edric Foy (UMRAE ), Antoine Deleforge (MULTISPEECH), Diego Di Carlo
(PANAMA)
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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