Deep learning based spatial aliasing reduction in beamforming for audio capture
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909855412912128 |
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| author | Guzik, Mateusz Cengarle, Giulio Arteaga, Daniel |
| author_facet | Guzik, Mateusz Cengarle, Giulio Arteaga, Daniel |
| contents | Spatial aliasing affects spaced microphone arrays, causing directional ambiguity above certain frequencies, degrading spatial and spectral accuracy of beamformers. Given the limitations of conventional signal processing and the scarcity of deep learning approaches to spatial aliasing mitigation, we propose a novel approach using a U-Net architecture to predict a signal-dependent de-aliasing filter, which reduces aliasing in conventional beamforming for spatial capture. Two types of multichannel filters are considered, one which treats the channels independently and a second one that models cross-channel dependencies. The proposed approach is evaluated in two common spatial capture scenarios: stereo and first-order Ambisonics. The results indicate a very significant improvement, both objective and perceptual, with respect to conventional beamforming. This work shows the potential of deep learning to reduce aliasing in beamforming, leading to improvements in multi-microphone setups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19781 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deep learning based spatial aliasing reduction in beamforming for audio capture Guzik, Mateusz Cengarle, Giulio Arteaga, Daniel Audio and Speech Processing Sound Spatial aliasing affects spaced microphone arrays, causing directional ambiguity above certain frequencies, degrading spatial and spectral accuracy of beamformers. Given the limitations of conventional signal processing and the scarcity of deep learning approaches to spatial aliasing mitigation, we propose a novel approach using a U-Net architecture to predict a signal-dependent de-aliasing filter, which reduces aliasing in conventional beamforming for spatial capture. Two types of multichannel filters are considered, one which treats the channels independently and a second one that models cross-channel dependencies. The proposed approach is evaluated in two common spatial capture scenarios: stereo and first-order Ambisonics. The results indicate a very significant improvement, both objective and perceptual, with respect to conventional beamforming. This work shows the potential of deep learning to reduce aliasing in beamforming, leading to improvements in multi-microphone setups. |
| title | Deep learning based spatial aliasing reduction in beamforming for audio capture |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2505.19781 |