MATEC Web Conf.
Volume 283, 2019The 2nd Franco-Chinese Acoustic Conference (FCAC 2018)
|Number of page(s)||5|
|Section||Ultrasounds, Signal Processing, and NDT/E|
|Published online||28 June 2019|
Nonlinear dimensionality reduction for the acoustic field measured by a linear sensor array
1 Department of Marine Technology, Ocean University of China, 266100, Qingdao, China
2 Department of Electronics, Ocean University of China, 266100, Qingdao, China
* Corresponding author: email@example.com
Dimensionality reduction is one of the central problems in machine learning and pattern recognition, which aims to develop a compact representation for complex data from high-dimensional observations. Here, we apply a nonlinear manifold learning algorithm, called local tangent space alignment (LTSA) algorithm, to high-dimensional acoustic observations and achieve nonlinear dimensionality reduction for the acoustic field measured by a linear senor array. By dimensionality reduction, the underlying physical degrees of freedom of acoustic field, such as the variations of sound source location and sound speed profiles, can be discovered. Two simulations are presented to verify the validity of the approach.
© The Authors, published by EDP Sciences, 2019
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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