# A Universal Deep Room Acoustics Estimator

arXiv: https://arxiv.org/abs/2109.14436 · HTML: https://mlsanity.com/paper/2109.14436/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2021.

## Built on by (most cited first, 4 of 4)

- [Dual input neural networks for positional sound source localization](https://mlsanity.com/paper/2308.04169/) (2023)
- [MOSRA: Joint Mean Opinion Score and Room Acoustics Speech Quality Assessment](https://mlsanity.com/paper/2204.01345/) (2022)
- [Efficient Speech Quality Assessment using Self-supervised Framewise Embeddings](https://mlsanity.com/paper/2211.06646/) (2022)
- [Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features](https://mlsanity.com/paper/2411.03172/) (2024)

## Abstract (arXiv)

Speech audio quality is subject to degradation caused by an acoustic environment and isotropic ambient and point noises. The environment can lead to decreased speech intelligibility and loss of focus and attention by the listener. Basic acoustic parameters that characterize the environment well are (i) signal-to-noise ratio (SNR), (ii) speech transmission index, (iii) reverberation time, (iv) clarity, and (v) direct-to-reverberant ratio. Except for the SNR, these parameters are usually derived from the Room Impulse Response (RIR) measurements; however, such measurements are often not available. This work presents a universal room acoustic estimator design based on convolutional recurrent neural networks that estimate the acoustic environment measurement blindly and jointly. Our results indicate that the proposed system is robust to non-stationary signal variations and outperforms current state-of-the-art methods.