在被动音调检测中增强噪声适应性,通过广泛感应场复合值卷积神经网络进行检测
Guolong Liang1,2,3, Yu Chen1,2,3, Jinjin Wang1,2,3
1National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
The Journal of the Acoustical Society of America
|June 26, 2024
概括
一个新的以注意力驱动的复杂价值U-Net可以在杂的声纳系统中改进被动音调检测. 这种方法增强了噪声弹性和通用化,用于弱音识别.
科学领域:
- 信号处理 信号处理
- 水下声学 水下声学
- 机器学习 机器学习
背景情况:
- 被动声纳系统需要强大的音调检测来识别目标.
- 现有的算法在杂环境中难以检测弱音.
- 传统方法的有效性有限,因此需要提高噪音抵抗力.
研究的目的:
- 引入一个以注意力驱动的复杂值的U-Net来增强被动音调检测.
- 提高音调检测算法中的噪声弹性和概括能力.
- 在具有挑战性的声学条件下有效检测弱音.
主要方法:
- 提出了一个以注意力驱动的复杂价值的U-Net架构.
- 集成的时间和和的注意力机制,以有效地扩大受感场.
- 利用复杂值运算符来利用振幅和相位信息.
- 实施了对称的下采样和上采样策略,以改进时间频率重建.
主要成果:
- 拟议的方法显示出对噪声的强烈稳定性.
- 该模型表现出在不同数据集中概括的显著能力.
- 实验结果验证了它在传统的U形结构上的优越性.
- 在模拟和现实数据中,可以有效检测弱音.
结论:
- 以注意力驱动的复杂值U-Net为被动音调检测提供了卓越的性能.
- 该方法为声纳应用提供了增强的噪声弹性和通用性.
- 这种方法提高了在复杂的声学环境中检测弱音的能力.
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