# DPLM: A Deep Perceptual Spatial-Audio Localization Metric

arXiv: https://arxiv.org/abs/2105.14180 · HTML: https://mlsanity.com/paper/2105.14180/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 May 2021.

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

- [PEAVS: Perceptual Evaluation of Audio-Visual Synchrony Grounded in Viewers' Opinion Scores](https://mlsanity.com/paper/2404.07336/) (2024)
- [End-to-End Binaural Speech Synthesis](https://mlsanity.com/paper/2207.03697/) (2022)

## Abstract (arXiv)

Subjective evaluations are critical for assessing the perceptual realism of sounds in audio-synthesis driven technologies like augmented and virtual reality. However, they are challenging to set up, fatiguing for users, and expensive. In this work, we tackle the problem of capturing the perceptual characteristics of localizing sounds. Specifically, we propose a framework for building a general purpose quality metric to assess spatial localization differences between two binaural recordings. We model localization similarity by utilizing activation-level distances from deep networks trained for direction of arrival (DOA) estimation. Our proposed metric (DPLM) outperforms baseline metrics on correlation with subjective ratings on a diverse set of datasets, even without the benefit of any human-labeled training data.