2025/06/23 by Diego Di Carlo, Di Carlo, Diego, Mathieu Fontaine +7
Computer Science · Earth and Planetary Sciences · Engineering · #Artificial Intelligence (cs.AI) #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Sound (cs.SD) #Speech and Audio Processing #Structural Health Monitoring Techniques #Underwater Acoustics Research #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2506.18954
openalex publication_date 2025/06/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.