# Environment Classification via Blind Roomprints Estimation

arXiv: https://arxiv.org/abs/2209.07196 · HTML: https://mlsanity.com/paper/2209.07196/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Sep 2022.

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

- [EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments](https://mlsanity.com/paper/2405.02119/) (2024)
- [RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classification](https://mlsanity.com/paper/2406.03120/) (2024)

## Abstract (arXiv)

In this paper we present a novel approach for environment classification for speech recordings, which does not require the selection of decaying reverberation tails. It is based on a multi-band RT60 analysis of blind channel estimates and achieves an accuracy of up to 93.6% on test recordings derived from the ACE corpus.