自主监督的基于学习的水下声信号分类通过面具建模
Kele Xu1, Qisheng Xu1, Kang You2
1National Key Laboratory of Parallel and Distributed Processing, Changsha, 410073, China.
The Journal of the Acoustical Society of America
|July 5, 2023
概括
这项研究引入了一种新的自我监督方法,用于使用深度学习对水下声学信号进行分类. 该方法有效地从未标记的数据中学习信号表示,即使在具有挑战性的条件下也能达到高精度.
科学领域:
- 信号处理 信号处理
- 机器学习 机器学习
- 声学 声学 在声学上
背景情况:
- 水下声信号分类对于军事和民用应用至关重要.
- 深度神经网络在分类方面表现出色,但信号表示尚未得到充分探索.
- 对于深度学习来说,大规模数据集的注释是昂贵和困难的.
研究的目的:
- 开发一种用于水下声信号分类的新型自我监督表示学习方法.
- 为深度学习模型解决信号表示和数据注释方面的挑战.
- 在各种音响环境中提高分类准确性和稳定性.
主要方法:
- 一个两阶段的方法:借口学习与未标记的数据和微调与有限的标记数据.
- 使用Swin变压器架构来重建掩盖日志Mel光谱图.
- 自主监督学习提取一般声信号表示.
主要成果:
- 在DeepShip数据集上实现了80.22%的分类准确度,超过了以前的方法.
- 在低信号噪声比 (SNR) 条件下表现出强的性能.
- 在短暂的学习场景中表现出有效性.
结论:
- 拟议的自我监督方法为水下声信号分类提供了有效的解决方案.
- 这种方法减少了对大型标记数据集的依赖,减轻了注释成本.
- 该方法提供了强大的分类性能,可适应多样化和具有挑战性的声环境.
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