SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908418612133888 |
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| author | Di Carlo, Diego Fontaine, Mathieu Nugraha, Aditya Arie Bando, Yoshiaki Yoshii, Kazuyoshi |
| author_facet | Di Carlo, Diego Fontaine, Mathieu Nugraha, Aditya Arie Bando, Yoshiaki Yoshii, Kazuyoshi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18954 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer Di Carlo, Diego Fontaine, Mathieu Nugraha, Aditya Arie Bando, Yoshiaki Yoshii, Kazuyoshi Sound Artificial Intelligence Machine Learning Audio and Speech Processing 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. |
| title | SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2506.18954 |