通过对比的伪学习,半监督方法用于波的船舶辐射噪声识别系统
Jianxin Wu1, Xingyu Zhang1, Ying Zhou2
1Faculty of Information Science and Engineering, Ocean University of China, Qingdao, 266100, China.
The Journal of the Acoustical Society of America
|May 2, 2025
概括
这项研究引入了船舶辐射噪声识别 (SRNR) 的新框架,即使在弱水下声信号下也能很好地工作. 该方法增强了无人驾驶平台在具有挑战性的条件下的自主导航和环境传感.
科学领域:
- 水下声学 水下声学
- 信号处理 信号处理
- 机器学习用于自主系统.
背景情况:
- 在无人驾驶移动平台上,水下声学目标识别至关重要.
- 现有的方法在声学识别中扎着低信号噪声比率 (SNR).
- 需要强大的识别系统,适合边缘计算.
研究的目的:
- 为船舶辐射噪声识别 (SRNR) 提出一种新的对比性伪学习框架.
- 开发一个数据驱动的SRNR系统,优化用于边缘设备,如波浪滑翔机.
- 在低SNR环境中提高声信号识别精度.
主要方法:
- 实现了一个时间频率软重量模块,用于特征融合和干扰抑制.
- 引入全频段-子频段对比学习以增强声学表示.
- 利用对比的伪标签用于半监督学习,使用大型未标签数据集.
主要成果:
- 拟议的系统在船舶辐射噪声识别方面展示了最先进的性能.
- 在杂的,真实的水下条件下达到高精度.
- 通过广泛的实验和海上试验来验证.
结论:
- 相反的伪学习框架显著提高了SRNR的性能.
- 该方法对于在具有挑战性的声学环境中的边缘计算设备是有效的.
- 推进无人驾驶平台的自主导航和环境传感能力.
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