SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
This work addresses sound localization for applications like audio processing, but it is incremental as it builds on existing models with a hybrid approach.
The paper tackles sound source localization by combining an α-stable model with a neural network to interpolate steering vectors, achieving improved performance over state-of-the-art methods for multiple sound sources as indicated by objective scores.
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 ($α$ $\in$ (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.