首页> 外文学位 >Acoustic Monitoring System for Frog Population Estimation Using In-Situ Progressive Learning.
【24h】

Acoustic Monitoring System for Frog Population Estimation Using In-Situ Progressive Learning.

机译:使用原位渐进学习的青蛙种群估计声监测系统。

获取原文
获取原文并翻译 | 示例

摘要

Frog populations are considered excellent bio-indicators and hence the ability to monitor changes in their populations can be very useful for ecological research and environmental monitoring. This thesis presents a new population estimation approach based on the recognition of individual frogs of the same species, namely the Pseudacris Regilla (Pacific Chorus Frog), which does not rely on the availability of prior training data. An in-situ progressive learning algorithm is developed to determine whether an incoming call belongs to a previously detected individual frog or a newly encountered individual frog. A temporal call overlap detector is also presented as a pre-processing tool to eliminate overlapping calls. This is done to prevent the degrading of the learning process. The approach uses Mel-frequency cepstral coefficients (MFCCs) and multivariate Gaussian models to achieve individual frog recognition.;In the first part of this thesis, the MFCC as well as the related linear predictive cepstral coefficients (LPCC) acoustic feature extraction processes are reviewed. The Gaussian mixture models (GMM) are also reviewed as an extension to the classical Gaussian modeling used in the proposed approach.;In the second part of this thesis, the proposed frog population estimation system is presented and discussed in detail. The proposed system involves several different components including call segmentation, feature extraction, overlap detection, and the in-situ progressive learning process.;In the third part of the thesis, data description and system performance results are provided. The process of synthetically generating test sequences of real frog calls, which are applied to the proposed system for performance analysis, is described. Also, the results of the system performance are presented which show that the system is successful in distinguishing individual frogs, hence capable of providing reasonable estimates of the frog population. The system can readily be transitioned for the purpose of actual field studies.
机译:青蛙种群被认为是出色的生物指标,因此监测种群变化的能力对于生态研究和环境监测非常有用。本文提出了一种新的种群估计方法,该方法基于对相同物种的个体青蛙即Pseudacris Regilla(Pacific Chorus Frog)的识别,该方法不依赖于先前训练数据的可用性。开发了一种现场渐进式学习算法,以确定传入呼叫属于先前检测到的单个青蛙还是新遇到的单个青蛙。还提出了时间呼叫重叠检测器作为消除重叠呼叫的预处理工具。这样做是为了防止学习过程降级。该方法使用梅尔频率倒谱系数(MFCC)和多元高斯模型来实现单个青蛙的识别。本文的第一部分对MFCC以及相关的线性预测倒谱系数(LPCC)声学特征提取过程进行了回顾。 。还对高斯混合模型(GMM)进行了回顾,作为对所提出方法使用的经典高斯模型的扩展。在本论文的第二部分,对提出的青蛙种群估计系统进行了详细介绍和讨论。所提出的系统包括呼叫分割,特征提取,重叠检测和原位渐进学习过程等几个不同部分。在论文的第三部分,提供了数据描述和系统性能结果。描述了综合生成真实青蛙调用的测试序列的过程,该过程应用于提出的系统进行性能分析。此外,系统性能的结果也表明系统可以成功地区分青蛙,因此能够提供合理的青蛙种群估计值。该系统可以很容易地转换以用于实际的现场研究。

著录项

  • 作者

    Aboudan, Adam.;

  • 作者单位

    Colorado State University.;

  • 授予单位 Colorado State University.;
  • 学科 Engineering Electronics and Electrical.;Physics Acoustics.
  • 学位 M.S.
  • 年度 2013
  • 页码 81 p.
  • 总页数 81
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号