鲸类回声定位咔嗒声检测算法综述
Review of Cetacean's click detection algorithms
AI总结：
本文综述了2002至2023年间鲸类回声定位咔嗒声的检测与分类算法，按方法论分类并比较性能，指出挑战与开放平台，为相关研究提供系统参考。
摘要：
回声定位咔嗒声的检测是理解鲸类复杂行为及监测其种群的关键。依赖咔嗒声进行导航、觅食甚至通讯的鲸类物种包括抹香鲸（Physeter macrocephalus）和多种海豚群体。回声定位咔嗒声是持续时间短的宽带信号，通常以变化的咔嗒声间间隔（ICI）序列形式发出。尽管已有针对咔嗒声的数据集和模型，但咔嗒声的检测与分类仍面临重大挑战，主要归因于咔嗒声结构的多样性、同时发声动物产生的信号重叠，以及来自例如鼓虾和船舶空化噪声等的大量瞬态噪声干扰。本文对2002年至2023年间众多咔嗒声检测与分类方法进行了综述。我们按方法论将所综述的技术分为若干类别，具体包括：特征分析（如相位、ICI和持续时间）、频率内容、基于能量的检测、有监督和无监督机器学习、模板匹配以及自适应检测方法。此外，还综述了用于咔嗒声检测的开放访问平台及可公开用于测试的数据库。文中给出了每篇论文所采用方法的细节及其优点和局限性，并对每个类别分析了尚存的挑战。本文还针对共享数据库对若干方案进行了性能比较。最后，我们提供了汇总表格，从所解决的挑战、方法、采用的检测与分类工具、使用的特征及应用等方面概述现有检测方案。
英文摘要：
The detection of echolocation clicks is key in understanding the intricate behaviors of cetaceans and monitoring their populations. Cetacean species relying on clicks for navigation, foraging and even communications are sperm whales (Physeter macrocephalus) and a variety of dolphin groups. Echolocation clicks are wideband signals of short duration that are often emitted in sequences of varying inter-click-intervals. While datasets and models for clicks exist, the detection and classification of clicks present a significant challenge, mostly due to the diversity of clicks' structures, overlapping signals from simultaneously emitting animals, and the abundance of noise transients from, for example, snapping shrimps and shipping cavitation noise. This paper provides a survey of the many detection and classification methodologies of clicks, ranging from 2002 to 2023. We divide the surveyed techniques into categories by their methodology. Specifically, feature analysis (e.g., phase, ICI and duration), frequency content, energy based detection, supervised and unsupervised machine learning, template matching and adaptive detection approaches. Also surveyed are open access platforms for click detections, and databases openly available for testing. Details of the method applied for each paper are given along with advantages and limitations, and for each category we analyze the remaining challenges. The paper also includes a performance comparison for several schemes over a shared database. Finally, we provide tables summarizing the existing detection schemes in terms of challenges address, methods, detection and classification tools applied, features used and applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
