基于形状统计特征的 XGBoost 分类器检测水中的典型瞬态信号:在南露脊鲸叫声中的应用

IF 2.7 3区 地球科学 Q1 ENGINEERING, MARINE Journal of Marine Science and Engineering Pub Date : 2024-09-09 DOI:10.3390/jmse12091596
Zemin Zhou, Yanrui Qu, Boqing Zhu, Bingbing Zhang
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引用次数: 0

摘要

鲸鱼的声音是一种典型的瞬态信号。生态研究和海洋保护的要求不断提高,需要采用先进的技术对水下声学信号进行自动检测和分类。传统的能量检测方法主要关注振幅,在海洋环境典型的非高斯噪声条件下往往表现不佳。本研究介绍了一种先分类后检测的方法,它克服了以振幅为重点的技术的局限性。我们还解决了深度学习模型带来的挑战,例如高昂的数据标记成本和大量的计算要求。通过从音频中提取形状统计特征并使用 XGBoost 分类器,我们的方法不仅在准确性上优于传统的卷积神经网络(CNN)方法,还降低了对标记数据的依赖,从而提高了检测效率。这些特征的整合大大提高了模型的性能,促进了海洋声学遥感技术的广泛应用。这项研究为海洋生物声学监测的发展做出了贡献,提供了一种可靠、快速、训练效率高且适合实际部署的方法。
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Detection of Typical Transient Signals in Water by XGBoost Classifier Based on Shape Statistical Features: Application to the Call of Southern Right Whale
Whale sound is a typical transient signal. The escalating demands of ecological research and marine conservation necessitate advanced technologies for the automatic detection and classification of underwater acoustic signals. Traditional energy detection methods, which focus primarily on amplitude, often perform poorly in the non-Gaussian noise conditions typical of oceanic environments. This study introduces a classified-before-detect approach that overcomes the limitations of amplitude-focused techniques. We also address the challenges posed by deep learning models, such as high data labeling costs and extensive computational requirements. By extracting shape statistical features from audio and using the XGBoost classifier, our method not only outperforms the traditional convolutional neural network (CNN) method in accuracy but also reduces the dependence on labeled data, thus improving the detection efficiency. The integration of these features significantly enhances model performance, promoting the broader application of marine acoustic remote sensing technologies. This research contributes to the advancement of marine bioacoustic monitoring, offering a reliable, rapid, and training-efficient method suitable for practical deployment.
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来源期刊
Journal of Marine Science and Engineering
Journal of Marine Science and Engineering Engineering-Ocean Engineering
CiteScore
4.40
自引率
20.70%
发文量
1640
审稿时长
18.09 days
期刊介绍: Journal of Marine Science and Engineering (JMSE; ISSN 2077-1312) is an international, peer-reviewed open access journal which provides an advanced forum for studies related to marine science and engineering. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
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