# SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer

arXiv: https://arxiv.org/abs/2506.18954 · HTML: https://mlsanity.com/paper/2506.18954/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments](https://mlsanity.com/paper/2107.04174/) (2021)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis](https://mlsanity.com/paper/2311.01052/) (2023)
- [Directional ASR: A New Paradigm for E2E Multi-Speaker Speech Recognition with Source Localization](https://mlsanity.com/paper/2011.00091/) (2020)
- [Differentiable Tracking-Based Training of Deep Learning Sound Source Localizers](https://mlsanity.com/paper/2111.00030/) (2021)
- [PILOT: Introducing Transformers for Probabilistic Sound Event Localization](https://mlsanity.com/paper/2106.03903/) (2021)
- [SLAM-based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization](https://mlsanity.com/paper/2405.19813/) (2024)
- [Learning Deep Direct-Path Relative Transfer Function for Binaural Sound Source Localization](https://mlsanity.com/paper/2202.07841/) (2022)
- [Sound Field Estimation around a Rigid Sphere with Physics-informed Neural Network](https://mlsanity.com/paper/2307.14013/) (2023)
- [Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions](https://mlsanity.com/paper/2305.04447/) (2023)

## Abstract (arXiv)

This paper describes a sound source localization (SSL) technique that combines an α-stable model for the observed signal with a neural network-based approach for modeling steering vectors. Specifically, a physics-informed neural network, referred to as Neural Steerer, is used to interpolate measured steering vectors (SVs) on a fixed microphone array. This allows for a more robust estimation of the so-called α-stable spatial measure, which represents the most plausible direction of arrival (DOA) of a target signal. As an α-stable model for the non-Gaussian case (α ∈ (0, 2)) theoretically defines a unique spatial measure, we choose to leverage it to account for residual reconstruction error of the Neural Steerer in the downstream tasks. The objective scores indicate that our proposed technique outperforms state-of-the-art methods in the case of multiple sound sources.