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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.02609 |
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| _version_ | 1866929577613328384 |
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| author | Arikan, Toros Chackalackal, Luca M. Ahsan, Fatima Tittel, Konrad Singer, Andrew C. Wornell, Gregory W. Baraniuk, Richard G. |
| author_facet | Arikan, Toros Chackalackal, Luca M. Ahsan, Fatima Tittel, Konrad Singer, Andrew C. Wornell, Gregory W. Baraniuk, Richard G. |
| contents | Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which outperformed the state-of-the-art, both in simulation and in real-life experimental settings. A limitation of this method was that some prior information had to be provided by the user on the number and locations of the reflective boundaries, and that its neural networks had to be re-trained accordingly for different environments. Utilizing more advanced neural network and time delay estimation techniques, the proposed improved method no longer requires prior knowledge the number of boundaries or their locations, and is able to estimate two-dimensional environments with one or two boundaries. Future work will extend the proposed method to more boundaries and larger-scale environments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_02609 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning Arikan, Toros Chackalackal, Luca M. Ahsan, Fatima Tittel, Konrad Singer, Andrew C. Wornell, Gregory W. Baraniuk, Richard G. Sound Audio and Speech Processing Underwater acoustic environment estimation is a challenging but important task for remote sensing scenarios. Current estimation methods require high signal strength and a solution to the fragile echo labeling problem to be effective. In previous publications, we proposed a general deep learning-based method for two-dimensional environment estimation which outperformed the state-of-the-art, both in simulation and in real-life experimental settings. A limitation of this method was that some prior information had to be provided by the user on the number and locations of the reflective boundaries, and that its neural networks had to be re-trained accordingly for different environments. Utilizing more advanced neural network and time delay estimation techniques, the proposed improved method no longer requires prior knowledge the number of boundaries or their locations, and is able to estimate two-dimensional environments with one or two boundaries. Future work will extend the proposed method to more boundaries and larger-scale environments. |
| title | Estimating the Number and Locations of Boundaries in Reverberant Environments with Deep Learning |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2411.02609 |