基于分解算法的海豚生物声纳信号频率和能量差检测

M. W. Muller
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引用次数: 0

摘要

一组先前从夏威夷卡内奥赫湾的大西洋宽吻海豚身上收集到的海豚回声定位信号,使用匹配追踪算法进行分解,以进一步研究四种类型的回声定位信号在[1]中概述的作用。该方法将回声定位信号分解为最优的线性波形展开,这些波形是字典中定义的Gabor函数。该方法允许研究海豚在识别任务期间功能带宽内频率内容的变化。我们研究了功能带宽在信号能级和回波定位任务性能方面的作用。此外,将ROC分析应用于匹配波形的相对能量以确定识别概率。结果表明,海豚可以通过检查目标之间的相关频率差异来区分。此外,ROC分析的结果提供了对不同种类的海豚信号的作用和修改发出的回声定位点击的重要性的见解,这可能是海豚识别和区分目标的能力的基础。
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Frequency and Energy Difference Detection of Dolphin Biosonar Signals Using a Decomposition Algorithm
A set of dolphin echolocation signals previously collected from an Atlantic bottlenose dolphin in Kaneohe Bay, Hawai’i are decomposed using a matching pursuit algorithm to further investigate the role of four types of echolocation signals outlined elsewhere [1]. The method decomposes the echolocation signals into optimal linear expansions of waveforms, which are Gabor functions defined in a dictionary. The method allows for study of the changes in frequency content within a dolphin’s functional bandwidth during discrimination tasks. We investigate the role of the functional bandwidth in terms of the signal energy levels and echolocations task performance. Furthermore, ROC analysis is applied to the relative energies of the matched waveforms to determine probability of discrimination. The results suggest that dolphins may discriminate by inspection of the relevant frequency differences between targets. In addition, the results from the ROC analysis provides insight into the role of the different classes of dolphin signals and of the importance of modification of the outgoing echolocation clicks, which may be fundamental to a dolphin’s ability to identify and discriminate targets.
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