将海洋哺乳动物的信号分类使用立方线间波,并结合三倍损失变化自动编码器
Nhat Hoang Bach1, Le Ha Vu2, Van Duc Nguyen3
1Institutes of Science and Technology, Institute of Electronics, Hanoi, 10000, Vietnam.
Scientific reports
|November 15, 2023
概括
这项研究引入了一种新的方法来分类海洋哺乳动物 (MM) 通信信号,使用立方-splines插值和罗神经网络-变异自动编码器 (SNN-VAE). 这种方法显著提高了水下声学信号的分类准确性.
科学领域:
- 声学信号处理 声学信号处理
- 机器学习用于海洋生物学
- 生物声学是一种生物声学.
背景情况:
- 对于被动声纳的传统时间频率方法 (Mel,STFT,WT) 对长期数据的分辨率和信息损失有局限性.
- 从声信号中提取特征频率,特别是海洋哺乳动物通信,需要先进的信号处理技术.
- 现有的自动编码器 (AE) 模型在数据解码过程中面临不连续性和完整性的挑战.
研究的目的:
- 开发一种改进的方法来分类海洋哺乳动物 (MM) 通信信号.
- 为了提高分辨率并减少声信号分析中的信息丢失.
- 克服传统时间频率转换和现有的深度学习模型的局限性.
主要方法:
- 这是一个两阶段的方法,它结合了立方支线插入 (CSI) 预处理和罗神经网络变量自动编码器 (SNN-VAE) 模型.
- 生成STFT-CSI光谱图,以加强特征频率之间的关系.
- 将Mel,STFT-CSI和Wavelet光谱图堆叠成一个特征光谱图,用于全面分析.
主要成果:
- 拟议的STFT-CSI光谱图增强了基于频率的特征连接.
- 通过SNN-VAE模型,海洋哺乳动物信号的分类准确度得到了提高.
- 与AE (2013) 相比,分类准确性增加了11%和20%,与NOAA数据集上的Resnet (2022) 相比增加了6%.
结论:
- 结合CSI预处理和SNN-VAE,为海洋哺乳动物信号分类提供了一种卓越的方法.
- 开发的技术有效地解决了长期声学数据中的分辨率和信息丢失问题.
- 这种方法为分析水下声通信提供了更强大,更准确的解决方案.
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