Multi-Channel Acoustic Echo Cancellation Based on Direction-of-Arrival Estimation
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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_ | 1866909640610021376 |
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| author | Zhao, Fei Zhang, Xueliang Wang, Zhong-Qiu |
| author_facet | Zhao, Fei Zhang, Xueliang Wang, Zhong-Qiu |
| contents | Acoustic echo cancellation (AEC) is an important speech signal processing technology that can remove echoes from microphone signals to enable natural-sounding full-duplex speech communication. While single-channel AEC is widely adopted, multi-channel AEC can leverage spatial cues afforded by multiple microphones to achieve better performance. Existing multi-channel AEC approaches typically combine beamforming with deep neural networks (DNN). This work proposes a two-stage algorithm that enhances multi-channel AEC by incorporating sound source directional cues. Specifically, a lightweight DNN is first trained to predict the sound source directions, and then the predicted directional information, multi-channel microphone signals, and single-channel far-end signal are jointly fed into an AEC network to estimate the near-end signal. Evaluation results show that the proposed algorithm outperforms baseline approaches and exhibits robust generalization across diverse acoustic environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_19493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Channel Acoustic Echo Cancellation Based on Direction-of-Arrival Estimation Zhao, Fei Zhang, Xueliang Wang, Zhong-Qiu Sound Audio and Speech Processing Acoustic echo cancellation (AEC) is an important speech signal processing technology that can remove echoes from microphone signals to enable natural-sounding full-duplex speech communication. While single-channel AEC is widely adopted, multi-channel AEC can leverage spatial cues afforded by multiple microphones to achieve better performance. Existing multi-channel AEC approaches typically combine beamforming with deep neural networks (DNN). This work proposes a two-stage algorithm that enhances multi-channel AEC by incorporating sound source directional cues. Specifically, a lightweight DNN is first trained to predict the sound source directions, and then the predicted directional information, multi-channel microphone signals, and single-channel far-end signal are jointly fed into an AEC network to estimate the near-end signal. Evaluation results show that the proposed algorithm outperforms baseline approaches and exhibits robust generalization across diverse acoustic environments. |
| title | Multi-Channel Acoustic Echo Cancellation Based on Direction-of-Arrival Estimation |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.19493 |