Machine learning for acoustic source localization in room and ocean acoustics

Machine learning for acoustic source localization in room and ocean acoustics

Despite decades of development, acoustic source localization has been challenged by mislocations. Instead, we learn the localizations directly from data using machine learning. After a mapping has been learned we test that the mapping generalizes well on test data. We demonstrate the approach in an...

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Veröffentlicht in:The Journal of the Acoustical Society of America 2021-10, Vol.150 (4), p.A112-A112
Hauptverfasser: Gerstoft, Peter, Niu, Haiqiang, Bianco, Michael J., Ozanich, Emma, Wu, Yifan
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container_issue 4
container_start_page A112
container_title The Journal of the Acoustical Society of America
container_volume 150
creator Gerstoft, Peter
Niu, Haiqiang
Bianco, Michael J.
Ozanich, Emma
Wu, Yifan
description Despite decades of development, acoustic source localization has been challenged by mislocations. Instead, we learn the localizations directly from data using machine learning. After a mapping has been learned we test that the mapping generalizes well on test data. We demonstrate the approach in an ocean waveguide as a classification and a regression neural network, in a room acoustic setting we demonstrate how a neural network can alleviate multipath.
doi_str_mv 10.1121/10.0007803
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title Machine learning for acoustic source localization in room and ocean acoustics
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