基于深度学习的泡分离,用于水下气体的被动声学监测 (esa)
Shuduo Liu1, Ben Liu2, Mengran Du2
1College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China.
The Journal of the Acoustical Society of America
|January 28, 2026
概括
这项研究引入了一种深度学习方法,用于将气泡声音与水下噪声分开,从而改善气体探测. 该技术增强了泡声音提取,即使在具有挑战性的低信号噪声比环境中也是如此.
科学领域:
- 声学 声学 在声学方面
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 被动声学监测 (PAM) 对于使用气泡共振研究水下气体羽毛非常有价值.
- 由于环境噪声干扰,传统的泡检测在低信号噪声比 (SNR) 的环境中扎.
研究的目的:
- 开发和验证一种基于深度学习 (DL) 的方法,用于将气泡声音与杂的水下声乐混合物分开.
- 通过在分析之前提取清洁的泡波形来提高在低SNR条件下泡检测的性能.
主要方法:
- 使用泡声学理论开发了一个数字模拟框架,用于生成用于训练DL模型的地面真相泡声音.
- 生成的泡声音与各种噪音类型混合,以创建多样化的训练数据集.
- 深度学习模型在模拟数据上进行了训练,并在模拟和现实世界被动声学监控记录上进行了测试.
主要成果:
- DL模型成功地提取了泡声音,即使在不同的噪音条件下,在时间频域中隐藏了特征.
- 应用到Haima冷漏的真实世界PAM数据显示,气泡释放率和气体羽附近的环境压力之间存在负相关性.
- 这些发现与现有文献一致,证实了拟议的基于DL的气泡声音分离方法的有效性.
结论:
- 拟议的基于DL的泡声音分离方法显著提高了在具有挑战性的水下声学环境中的泡检测性能.
- 该研究验证了DL方法,使用模拟数据和来自气体泄漏的现实世界被动声学监测记录.
- 该方法为分析水下气体提供了强大的解决方案,并有助于更好地了解气体漏动态.
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