一个高效的深度学习方法与频率和频道优化用于水下声学目标识别
Di Zeng1,2, Shefeng Yan1,2, Jirui Yang1,2
1University of Chinese Academy of Sciences, Beijing, 101408, China.
Scientific reports
|July 27, 2025
概括
本研究介绍了FCResNet5,这是用于船舶辐射噪声识别的新深度学习模型. 它为水下声信号分类提供了计算效率高,准确的解决方案.
科学领域:
- 声学信号处理 声学信号处理
- 深度学习用于水下声学
- 机器学习用于环境监测
背景情况:
- 由于背景噪声和宽信号频率,船舶辐射噪声 (SRN) 的识别很困难.
- 当前的深度学习模型是计算密集型的,并且使用不适合RGB频道用于SRN数据.
研究的目的:
- 为高效和准确的SRN分类开发一个优化的神经网络.
- 改进光谱表示,减少SRN识别中的计算负载.
主要方法:
- 提出了FCResNet5,一个精简的神经网络架构.
- 实施频率道化以增强光谱表示.
- 专注于与SRN相关的关键频段.
主要成果:
- FCResNet5实现了与现有模型相比较的准确性,具有更高的计算效率.
- 废弃性研究验证了单个模型组件的有效性.
- 对比分析证实FCResNet5是一个优越的替代方案.
结论:
- FCResNet5为船舶辐射噪声分类提供了高效有效的解决方案.
- 该模型的设计解决了水下声学当前深度学习方法的局限性.
- 优化的光谱表示和精简的架构是性能的关键.
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