# AuralNet: Hierarchical Attention-based 3D Binaural Localization of Overlapping Speakers

arXiv: https://arxiv.org/abs/2506.02773 · HTML: https://mlsanity.com/paper/2506.02773/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- First posted on arXiv 3 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Spatial Speech Perception Systems: A Survey of Sound Source Localization, Directional Enhancement, and Speech Recognition](https://mlsanity.com/paper/2607.02296/) (2026)

## Abstract (arXiv)

We propose AuralNet, a novel 3D multi-source binaural sound source localization approach that localizes overlapping sources in both azimuth and elevation without prior knowledge of the number of sources. AuralNet employs a gated coarse-tofine architecture, combining a coarse classification stage with a fine-grained regression stage, allowing for flexible spatial resolution through sector partitioning. The model incorporates a multi-head self-attention mechanism to capture spatial cues in binaural signals, enhancing robustness in noisy-reverberant environments. A masked multi-task loss function is designed to jointly optimize sound detection, azimuth, and elevation estimation. Extensive experiments in noisy-reverberant conditions demonstrate the superiority of AuralNet over recent methods