Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913970397380608 |
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| author | Shetu, Shrishti Saha Desiraju, Naveen Kumar Mack, Wolfgang Habets, Emanuël A. P. |
| author_facet | Shetu, Shrishti Saha Desiraju, Naveen Kumar Mack, Wolfgang Habets, Emanuël A. P. |
| contents | The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13620 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction Shetu, Shrishti Saha Desiraju, Naveen Kumar Mack, Wolfgang Habets, Emanuël A. P. Audio and Speech Processing Sound Signal Processing The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in real-life applications. In this work, we propose a hybrid approach to enhance the state-of-the-art (SOTA) ULCNet model by integrating time alignment and parallel encoder blocks for the model inputs, resulting in better echo reduction and comparable noise reduction performance to existing SOTA methods. We also propose a channel-wise sampling-based feature reorientation method, ensuring robust performance across many challenging scenarios, while maintaining overall low computational and memory requirements. |
| title | Align-ULCNet: Towards Low-Complexity and Robust Acoustic Echo and Noise Reduction |
| topic | Audio and Speech Processing Sound Signal Processing |
| url | https://arxiv.org/abs/2410.13620 |