# A data-driven two-microphone method for in-situ sound absorption measurements

arXiv: https://arxiv.org/abs/2502.04143 · HTML: https://mlsanity.com/paper/2502.04143/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 59 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Machine learning in acoustics: theory and applications](https://mlsanity.com/paper/1905.04418/) (2019)

## Built on by (most cited first, 2 of 2)

- [Surface impedance inference via neural fields and sparse acoustic data obtained by a compact array](https://mlsanity.com/paper/2602.11425/) (2026)
- [Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers](https://mlsanity.com/paper/2604.07412/) (2026)

## Abstract (arXiv)

This work presents a data-driven approach to estimating the sound absorption coefficient of an infinite porous slab using a neural network and a two-microphone measurement on a finite porous sample. A 1D-convolutional network predicts the sound absorption coefficient from the complex-valued transfer function between the sound pressure measured at the two microphone positions. The network is trained and validated with numerical data generated by a boundary element model using the Delany-Bazley-Miki model, demonstrating accurate predictions for various numerical samples. The method is experimentally validated with baffled rectangular samples of a fibrous material, where sample size and source height are varied. The results show that the neural network offers the possibility to reliably predict the in-situ sound absorption of a porous material using the traditional two-microphone method as if the sample were infinite. The normal-incidence sound absorption coefficient obtained by the network compares well with that obtained theoretically and in an impedance tube. The proposed method has promising perspectives for estimating the sound absorption coefficient of acoustic materials after installation and in realistic operational conditions.