Online neural fusion of distortionless differential beamformers for robust speech enhancement
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908617268002816 |
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| author | Qian, Yuanhang Zhao, Kunlong Jin, Jilu Luo, Xueqin Huang, Gongping Chen, Jingdong Benesty, Jacob |
| author_facet | Qian, Yuanhang Zhao, Kunlong Jin, Jilu Luo, Xueqin Huang, Gongping Chen, Jingdong Benesty, Jacob |
| contents | Fixed beamforming is widely used in practice since it does not depend on the estimation of noise statistics and provides relatively stable performance. However, a single beamformer cannot adapt to varying acoustic conditions, which limits its interference suppression capability. To address this, adaptive convex combination (ACC) algorithms have been introduced, where the outputs of multiple fixed beamformers are linearly combined to improve robustness. Nevertheless, ACC often fails in highly non-stationary scenarios, such as rapidly moving interference, since its adaptive updates cannot reliably track rapid changes. To overcome this limitation, we propose a frame-online neural fusion framework for multiple distortionless differential beamformers, which estimates the combination weights through a neural network. Compared with conventional ACC, the proposed method adapts more effectively to dynamic acoustic environments, achieving stronger interference suppression while maintaining the distortionless constraint. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_24497 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Online neural fusion of distortionless differential beamformers for robust speech enhancement Qian, Yuanhang Zhao, Kunlong Jin, Jilu Luo, Xueqin Huang, Gongping Chen, Jingdong Benesty, Jacob Sound Artificial Intelligence Audio and Speech Processing Fixed beamforming is widely used in practice since it does not depend on the estimation of noise statistics and provides relatively stable performance. However, a single beamformer cannot adapt to varying acoustic conditions, which limits its interference suppression capability. To address this, adaptive convex combination (ACC) algorithms have been introduced, where the outputs of multiple fixed beamformers are linearly combined to improve robustness. Nevertheless, ACC often fails in highly non-stationary scenarios, such as rapidly moving interference, since its adaptive updates cannot reliably track rapid changes. To overcome this limitation, we propose a frame-online neural fusion framework for multiple distortionless differential beamformers, which estimates the combination weights through a neural network. Compared with conventional ACC, the proposed method adapts more effectively to dynamic acoustic environments, achieving stronger interference suppression while maintaining the distortionless constraint. |
| title | Online neural fusion of distortionless differential beamformers for robust speech enhancement |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2510.24497 |