# Contrastive Representation Learning for Acoustic Parameter Estimation

arXiv: https://arxiv.org/abs/2302.11205 · HTML: https://mlsanity.com/paper/2302.11205/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2023.

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

- [Blind Spatial Impulse Response Generation from Separate Room- and Scene-Specific Information](https://mlsanity.com/paper/2409.14971/) (2024)
- [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)
- [Quantifying the Uncertainty of Blindly Estimated Room Embeddings Using a Dispersion-Calibrated Score](https://mlsanity.com/paper/2607.01527/) (2026)

## Abstract (arXiv)

A study is presented in which a contrastive learning approach is used to extract low-dimensional representations of the acoustic environment from single-channel, reverberant speech signals. Convolution of room impulse responses (RIRs) with anechoic source signals is leveraged as a data augmentation technique that offers considerable flexibility in the design of the upstream task. We evaluate the embeddings across three different downstream tasks, which include the regression of acoustic parameters reverberation time RT60 and clarity index C50, and the classification into small and large rooms. We demonstrate that the learned representations generalize well to unseen data and perform similarly to a fully-supervised baseline.