GIBBONFINDR：用于声学信号检测和分类的R软件包
GIBBONFINDR: An R package for the detection and classification of acoustic signals
摘要：
最近在记录技术，数据存储和电池寿命的改进，导致了在使用被动声监测的各种研究问题的兴趣增加。在陆地环境中实施大规模声学监测计划的主要障碍之一是缺乏用于处理大型声音档案的用户友好的开源程序。在这里，我们描述了新的开源R包GIBBONFINDR，它具有在R编程环境中使用各种现成的机器学习算法进行声信号检测，分类和可视化的功能。我们提供了一个案例研究，展示了如何GIBBONFINDR功能可以在工作流程中使用，以检测和分类婆罗洲长臂猿（长臂猿muelleri）的长期声学数据集记录在丹浓谷保护区，沙巴，马来西亚的电话。机器学习是目前发展最快的领域之一-在许多学科中的应用-我们的目标是使常用的信号处理技术和机器学习算法随时可供生态学家谁有兴趣将生物声学技术纳入他们的研究。
英文摘要：
The recent improvements in recording technology, data storage and battery life have led to an increased interest in the use of passive acoustic monitoring for a variety of research questions. One of the main obstacles in implementing wide scale acoustic monitoring programs in terrestrial environments is the lack of user-friendly, open source programs for processing large sound archives. Here we describe the new, open-source R package GIBBONFINDR which has functions for detection, classification and visualization of acoustic signals using a variety of readily available machine learning algorithms in the R programming environment. We provide a case study showing how GIBBONFINDR functions can be used in a workflow to detect and classify Bornean gibbon (Hylobates muelleri) calls in long-term acoustic data sets recorded in Danum Valley Conservation Area, Sabah, Malaysia. Machine learning is currently one of the most rapidly growing fields-- with applications across many disciplines-- and our goal is to make commonly used signal processing techniques and machine learning algorithms readily available for ecologists who are interested in incorporating bioacoustics techniques into their research.
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。
