A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866915312349216768 |
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| author | Wu, Yuan-Kuei Azcarreta, Juan Patel, Kashyap Xu, Buye Lee, Jung-Suk Lee, Sanha Pandey, Ashutosh |
| author_facet | Wu, Yuan-Kuei Azcarreta, Juan Patel, Kashyap Xu, Buye Lee, Jung-Suk Lee, Sanha Pandey, Ashutosh |
| contents | This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergence when dealing with highly correlated noise such as feedback. We introduce a Convolutional Recurrent Network that efficiently combines spatial and temporal processing, significantly enhancing speech enhancement capabilities with lower computational demands. Our approach utilizes three training methods: In-a-Loop Training, Teacher Forcing, and a Hybrid strategy with a Multichannel Wiener Filter, optimizing performance in complex acoustic environments. This scalable framework offers a robust solution for real-world applications, making significant advances in Acoustic Feedback Control technology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15914 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control Wu, Yuan-Kuei Azcarreta, Juan Patel, Kashyap Xu, Buye Lee, Jung-Suk Lee, Sanha Pandey, Ashutosh Sound Audio and Speech Processing This study presents a deep-learning framework for controlling multichannel acoustic feedback in audio devices. Traditional digital signal processing methods struggle with convergence when dealing with highly correlated noise such as feedback. We introduce a Convolutional Recurrent Network that efficiently combines spatial and temporal processing, significantly enhancing speech enhancement capabilities with lower computational demands. Our approach utilizes three training methods: In-a-Loop Training, Teacher Forcing, and a Hybrid strategy with a Multichannel Wiener Filter, optimizing performance in complex acoustic environments. This scalable framework offers a robust solution for real-world applications, making significant advances in Acoustic Feedback Control technology. |
| title | A Novel Deep Learning Framework for Efficient Multichannel Acoustic Feedback Control |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2505.15914 |