LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866912489572139008 |
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| author | Biswas, Subrata Khan, Mohammad Nur Hossain Colwell, Violet Adiletta, Jack Islam, Bashima |
| author_facet | Biswas, Subrata Khan, Mohammad Nur Hossain Colwell, Violet Adiletta, Jack Islam, Bashima |
| contents | Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from missing data due to the stochastic nature of energy harvesting, degrading localization performance. We propose LOCUS, a deep learning framework that recovers corrupted features in such settings. LOCUS integrates three modules: (1) Information-Weighted Focus (InFo) to identify corrupted regions, (2) Latent Feature Synthesizer (LaFS) to reconstruct missing features, and (3) Guided Replacement (GRep) to restore data without altering valid inputs. LOCUS significantly improves DoA accuracy under missing-channel conditions, achieving up to 36.91% error reduction on DCASE and LargeSet, and 25.87-59.46% gains in real-world deployments. We release a 50-hour multichannel dataset to support future research on localization under energy constraints. Our code and data are available at: https://bashlab.github.io/locus_project/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_09409 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy Biswas, Subrata Khan, Mohammad Nur Hossain Colwell, Violet Adiletta, Jack Islam, Bashima Machine Learning Accurate sound source localization (SSL), such as direction-of-arrival (DoA) estimation, relies on consistent multichannel data. However, batteryless systems often suffer from missing data due to the stochastic nature of energy harvesting, degrading localization performance. We propose LOCUS, a deep learning framework that recovers corrupted features in such settings. LOCUS integrates three modules: (1) Information-Weighted Focus (InFo) to identify corrupted regions, (2) Latent Feature Synthesizer (LaFS) to reconstruct missing features, and (3) Guided Replacement (GRep) to restore data without altering valid inputs. LOCUS significantly improves DoA accuracy under missing-channel conditions, achieving up to 36.91% error reduction on DCASE and LargeSet, and 25.87-59.46% gains in real-world deployments. We release a 50-hour multichannel dataset to support future research on localization under energy constraints. Our code and data are available at: https://bashlab.github.io/locus_project/ |
| title | LOCUS: LOcalization with Channel Uncertainty and Sporadic Energy |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2302.09409 |