# RevRIR: Joint Reverberant Speech and Room Impulse Response Embedding using Contrastive Learning with Application to Room Shape Classification

arXiv: https://arxiv.org/abs/2406.03120 · HTML: https://mlsanity.com/paper/2406.03120/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [AST: Audio Spectrogram Transformer](https://mlsanity.com/paper/2104.01778/) (2021)
- [MuLan: A Joint Embedding of Music Audio and Natural Language](https://mlsanity.com/paper/2208.12415/) (2022)
- [Contrastive Representation Learning for Acoustic Parameter Estimation](https://mlsanity.com/paper/2302.11205/) (2023)
- [End-to-End Classification of Reverberant Rooms using DNNs](https://mlsanity.com/paper/1812.09324/) (2018)
- [Environment Classification via Blind Roomprints Estimation](https://mlsanity.com/paper/2209.07196/) (2022)
- [Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework](https://mlsanity.com/paper/2307.01715/) (2023)
- [Attention Is All You Need For Blind Room Volume Estimation](https://mlsanity.com/paper/2309.13504/) (2023)

## Abstract (arXiv)

This paper focuses on room fingerprinting, a task involving the analysis of an audio recording to determine the specific volume and shape of the room in which it was captured. While it is relatively straightforward to determine the basic room parameters from the Room Impulse Responses (RIR), doing so from a speech signal is a cumbersome task. To address this challenge, we introduce a dual-encoder architecture that facilitates the estimation of room parameters directly from speech utterances. During pre-training, one encoder receives the RIR while the other processes the reverberant speech signal. A contrastive loss function is employed to embed the speech and the acoustic response jointly. In the fine-tuning stage, the specific classification task is trained. In the test phase, only the reverberant utterance is available, and its embedding is used for the task of room shape classification. The proposed scheme is extensively evaluated using simulated acoustic environments.