# Sound Source Distance Estimation in Diverse and Dynamic Acoustic Conditions

arXiv: https://arxiv.org/abs/2309.09288 · HTML: https://mlsanity.com/paper/2309.09288/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2023.

## Built on by (most cited first, 8 of 8)

- [Sound Event Detection and Localization with Distance Estimation](https://mlsanity.com/paper/2403.11827/) (2024)
- [Spatial Scaper: A Library to Simulate and Augment Soundscapes for Sound Event Localization and Detection in Realistic Rooms](https://mlsanity.com/paper/2401.12238/) (2024)
- [Diff-SAGe: End-to-End Spatial Audio Generation Using Diffusion Models](https://mlsanity.com/paper/2410.11299/) (2024)
- [Stereo Sound Event Localization and Detection with Onscreen/offscreen Classification](https://mlsanity.com/paper/2507.12042/) (2025)
- [Enhanced Sound Event Localization and Detection in Real 360-degree audio-visual soundscapes](https://mlsanity.com/paper/2401.17129/) (2024)
- [Latent Acoustic Mapping for Direction of Arrival Estimation: A Self-Supervised Approach](https://mlsanity.com/paper/2507.07066/) (2025)
- [Feature Aggregation in Joint Sound Classification and Localization Neural Networks](https://mlsanity.com/paper/2310.19063/) (2023)
- [An Experimental Study on Joint Modeling for Sound Event Localization and Detection with Source Distance Estimation](https://mlsanity.com/paper/2501.10755/) (2025)

## Abstract (arXiv)

Localizing a moving sound source in the real world involves determining its direction-of-arrival (DOA) and distance relative to a microphone. Advancements in DOA estimation have been facilitated by data-driven methods optimized with large open-source datasets with microphone array recordings in diverse environments. In contrast, estimating a sound source's distance remains understudied. Existing approaches assume recordings by non-coincident microphones to use methods that are susceptible to differences in room reverberation. We present a CRNN able to estimate the distance of moving sound sources across multiple datasets featuring diverse rooms, outperforming a recently-published approach. We also characterize our model's performance as a function of sound source distance and different training losses. This analysis reveals optimal training using a loss that weighs model errors as an inverse function of the sound source true distance. Our study is the first to demonstrate that sound source distance estimation can be performed across diverse acoustic conditions using deep learning.