# Similarity Metrics For Late Reverberation

arXiv: https://arxiv.org/abs/2408.14836 · HTML: https://mlsanity.com/paper/2408.14836/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2024.

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

- [EchoMark: Perceptual Acoustic Environment Transfer with Watermark-Embedded Room Impulse Response](https://mlsanity.com/paper/2511.06458/) (2025)
- [Ambisonics Encoding of Room Impulse Responses using a Device-Agnostic Diffusion Model](https://mlsanity.com/paper/2608.14097/) (2026)
- [Estimation of Room Impulse Responses from Handclaps](https://mlsanity.com/paper/2609.35839/) (2026)

## Abstract (arXiv)

Automatic tuning of reverberation algorithms relies on the optimization of a cost function. While general audio similarity metrics are useful, they are not optimized for the specific statistical properties of reverberation in rooms. This paper presents two novel metrics for assessing the similarity of late reverberation in room impulse responses. These metrics are differentiable and can be utilized within a machine-learning framework. We compare the performance of these metrics to two popular audio metrics using a large dataset of room impulse responses encompassing various room configurations and microphone positions. The results indicate that the proposed functions based on averaged power and frequency-band energy decay outperform the baselines with the former exhibiting the most suitable profile towards the minimum. The proposed work holds promise as an improvement to the design and evaluation of reverberation similarity metrics.