A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913859460136960 |
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| author | Wang, Zheng Rong, Xiaobin Sun, Yu Sun, Tianchi Lin, Zhibin Lu, Jing |
| author_facet | Wang, Zheng Rong, Xiaobin Sun, Yu Sun, Tianchi Lin, Zhibin Lu, Jing |
| contents | Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resources, particularly in low signal-to-noise ratio (SNR) conditions. In this paper, we propose a lightweight hybrid dual-channel speech enhancement system that combines independent vector analysis (IVA) with a modified version of the dual-channel grouped temporal convolutional recurrent network (GTCRN). IVA functions as a coarse estimator, providing auxiliary information for both speech and noise, while the modified GTCRN further refines the speech quality. We investigate several modifications to ensure the comprehensive utilization of both original and auxiliary information. Experimental results demonstrate the effectiveness of the proposed system, achieving enhanced speech with minimal parameters and low computational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions Wang, Zheng Rong, Xiaobin Sun, Yu Sun, Tianchi Lin, Zhibin Lu, Jing Audio and Speech Processing Although deep learning based multi-channel speech enhancement has achieved significant advancements, its practical deployment is often limited by constrained computational resources, particularly in low signal-to-noise ratio (SNR) conditions. In this paper, we propose a lightweight hybrid dual-channel speech enhancement system that combines independent vector analysis (IVA) with a modified version of the dual-channel grouped temporal convolutional recurrent network (GTCRN). IVA functions as a coarse estimator, providing auxiliary information for both speech and noise, while the modified GTCRN further refines the speech quality. We investigate several modifications to ensure the comprehensive utilization of both original and auxiliary information. Experimental results demonstrate the effectiveness of the proposed system, achieving enhanced speech with minimal parameters and low computational complexity. |
| title | A Lightweight Hybrid Dual Channel Speech Enhancement System under Low-SNR Conditions |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.19597 |