# Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings

arXiv: https://arxiv.org/abs/2107.13832 · HTML: https://mlsanity.com/paper/2107.13832/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [dEchorate: a Calibrated Room Impulse Response Database for Echo-aware Signal Processing](https://mlsanity.com/paper/2104.13168/) (2021)
- [Mirage: 2D Source Localization Using Microphone Pair Augmentation with Echoes](https://mlsanity.com/paper/1906.08968/) (2019)
- [Joint Estimation of Reverberation Time and Direct-to-Reverberation Ratio from Speech using Auditory-Inspired Features](https://mlsanity.com/paper/1510.04620/) (2015)
- [Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning](https://mlsanity.com/paper/1609.09747/) (2016)

## Built on by (most cited first, 3 of 3)

- [Spatial LibriSpeech: An Augmented Dataset for Spatial Audio Learning](https://mlsanity.com/paper/2308.09514/) (2023)
- [Blind Acoustic Room Parameter Estimation Using Phase Features](https://mlsanity.com/paper/2303.07449/) (2023)
- [Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features](https://mlsanity.com/paper/2411.03172/) (2024)

## Abstract (arXiv)

Knowing the geometrical and acoustical parameters of a room may benefit applications such as audio augmented reality, speech dereverberation or audio forensics. In this paper, we study the problem of jointly estimating the total surface area, the volume, as well as the frequency-dependent reverberation time and mean surface absorption of a room in a blind fashion, based on two-channel noisy speech recordings from multiple, unknown source-receiver positions. A novel convolutional neural network architecture leveraging both single- and inter-channel cues is proposed and trained on a large, realistic simulated dataset. Results on both simulated and real data show that using multiple observations in one room significantly reduces estimation errors and variances on all target quantities, and that using two channels helps the estimation of surface and volume. The proposed model outperforms a recently proposed blind volume estimation method on the considered datasets.