# Blind Acoustic Room Parameter Estimation Using Phase Features

arXiv: https://arxiv.org/abs/2303.07449 · HTML: https://mlsanity.com/paper/2303.07449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 29 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [Building and Evaluation of a Real Room Impulse Response Dataset](https://mlsanity.com/paper/1811.06795/) (2018)
- [Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments](https://mlsanity.com/paper/1705.10874/) (2017)
- [Single-Channel Maximum-Likelihood T60 Estimation Exploiting Subband Information](https://mlsanity.com/paper/1511.04063/) (2015)
- [Speech Dereverberation Using Nonnegative Convolutive Transfer Function and Spectro temporal Modeling](https://mlsanity.com/paper/1709.05557/) (2017)
- [Sound Event Detection in Urban Audio With Single and Multi-Rate PCEN](https://mlsanity.com/paper/2102.03468/) (2021)
- [Blind Room Parameter Estimation Using Multiple-Multichannel Speech Recordings](https://mlsanity.com/paper/2107.13832/) (2021)
- [Joint Estimation of Reverberation Time and Direct-to-Reverberation Ratio from Speech using Auditory-Inspired Features](https://mlsanity.com/paper/1510.04620/) (2015)
- [Blind Reverberation Time Estimation in Dynamic Acoustic Conditions](https://mlsanity.com/paper/2202.11790/) (2022)
- [Phase Continuity: Learning Derivatives of Phase Spectrum for Speech Enhancement](https://mlsanity.com/paper/2202.11918/) (2022)
- [Acoustic Room Compensation Using Local PCA-based Room Average Power Response Estimation](https://mlsanity.com/paper/2206.15356/) (2022)
- [Realistic sources, receivers and walls improve the generalisability of virtually-supervised blind acoustic parameter estimators](https://mlsanity.com/paper/2207.09133/) (2022)

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

- [Gencho: Room Impulse Response Generation from Reverberant Speech and Text via Diffusion Transformers](https://mlsanity.com/paper/2602.09233/) (2026)
- [Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features](https://mlsanity.com/paper/2411.03172/) (2024)
- [EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments](https://mlsanity.com/paper/2405.02119/) (2024)

## Abstract (arXiv)

Modeling room acoustics in a field setting involves some degree of blind parameter estimation from noisy and reverberant audio. Modern approaches leverage convolutional neural networks (CNNs) in tandem with time-frequency representation. Using short-time Fourier transforms to develop these spectrogram-like features has shown promising results, but this method implicitly discards a significant amount of audio information in the phase domain. Inspired by recent works in speech enhancement, we propose utilizing novel phase-related features to extend recent approaches to blindly estimate the so-called "reverberation fingerprint" parameters, namely, volume and RT60. The addition of these features is shown to outperform existing methods that rely solely on magnitude-based spectral features across a wide range of acoustics spaces. We evaluate the effectiveness of the deployment of these novel features in both single-parameter and multi-parameter estimation strategies, using a novel dataset that consists of publicly available room impulse responses (RIRs), synthesized RIRs, and in-house measurements of real acoustic spaces.