Beyond Omnidirectional: Neural Ambisonics Encoding for Arbitrary Microphone Directivity Patterns using Cross-Attention
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914295921508352 |
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| author | Heikkinen, Mikko Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas |
| author_facet | Heikkinen, Mikko Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas |
| contents | We present a deep neural network approach for encoding microphone array signals into Ambisonics that generalizes to arbitrary microphone array configurations with fixed microphone count but varying locations and frequency-dependent directional characteristics. Unlike previous methods that rely only on array geometry as metadata, our approach uses directional array transfer functions, enabling accurate characterization of real-world arrays. The proposed architecture employs separate encoders for audio and directional responses, combining them through cross-attention mechanisms to generate array-independent spatial audio representations. We evaluate the method on simulated data in two settings: a mobile phone with complex body scattering, and a free-field condition, both with varying numbers of sound sources in reverberant environments. Evaluations demonstrate that our approach outperforms both conventional digital signal processing-based methods and existing deep neural network solutions. Furthermore, using array transfer functions instead of geometry as metadata input improves accuracy on realistic arrays. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_23196 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Beyond Omnidirectional: Neural Ambisonics Encoding for Arbitrary Microphone Directivity Patterns using Cross-Attention Heikkinen, Mikko Politis, Archontis Drossos, Konstantinos Virtanen, Tuomas Audio and Speech Processing We present a deep neural network approach for encoding microphone array signals into Ambisonics that generalizes to arbitrary microphone array configurations with fixed microphone count but varying locations and frequency-dependent directional characteristics. Unlike previous methods that rely only on array geometry as metadata, our approach uses directional array transfer functions, enabling accurate characterization of real-world arrays. The proposed architecture employs separate encoders for audio and directional responses, combining them through cross-attention mechanisms to generate array-independent spatial audio representations. We evaluate the method on simulated data in two settings: a mobile phone with complex body scattering, and a free-field condition, both with varying numbers of sound sources in reverberant environments. Evaluations demonstrate that our approach outperforms both conventional digital signal processing-based methods and existing deep neural network solutions. Furthermore, using array transfer functions instead of geometry as metadata input improves accuracy on realistic arrays. |
| title | Beyond Omnidirectional: Neural Ambisonics Encoding for Arbitrary Microphone Directivity Patterns using Cross-Attention |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2601.23196 |