Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment
文献类型：期刊论文
| 作者 | Wang WB(王文博); Wang Z(王臻); Su L(苏林); Hu T(胡涛); Ren QY(任群言); Peter Gerstoft2; Ma L(马力) |
| 刊名 | The Journal of the Acoustical Society of America
 |
| 出版日期 | 2020 |
| 期号 | 未知页码:3633 |
| DOI | 10.1121/10.0002911 |
| 英文摘要 | Multiple approaches for depth estimation in deep-ocean environments are discussed. First, a multispectral transformation for depth estimation (MSTDE) method based on the low-spatial-frequency interference in a constant sound speed is derived to estimate the source depth directly. To overcome the limitation of real sound-speed profiles and source bandwidths on the accuracy of MSTDE, a method based on a convolution neural network (CNN) and conventional beamforming (CBF) preprocessing is proposed. Further, transfer learning is adapted to tackle the effect of noise on the estimation result. At-sea data are used to test the performance of these methods, and results suggest that (1) the MSTDE can estimate the depth; however, the error increases with distance; (2) MSTDE error can be moderately compensated through a calculated factor; (3) the performance of deep-learning approach using CBF preprocessing is much better than those of MSTDE and traditional CNN. |
| 源URL | [http://159.226.59.140/handle/311008/9507]  |
| 专题 | 历年期刊论文_2020年期刊论文 |
| 作者单位 | 中国科学院声学研究所 |
| 推荐引用方式 GB/T 7714 | 王文博；王臻；苏林；胡涛；任群言；Peter Gerstoft2；马力. Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment[J]. The Journal of the Acoustical Society of America,2020(未知):3633. |
| APA | 王文博；王臻；苏林；胡涛；任群言；Peter Gerstoft2；马力.(2020).Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment.The Journal of the Acoustical Society of America(未知),3633. |
| MLA | 王文博；王臻；苏林；胡涛；任群言；Peter Gerstoft2；马力."Source depth estimation using spectral transformations and convolutional neural network in a deep-sea environment".The Journal of the Acoustical Society of America .未知(2020):3633. |
入库方式： OAI收割
来源：声学研究所
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