Erbe C, King A R, Yedlin M, Farmer D M
Institute of Ocean Sciences, Sidney, British Columbia, Canada.
J Acoust Soc Am. 1999 May;105(5):2967-78. doi: 10.1121/1.426945.
Environmental assessments of manmade noise and its effects on marine mammals need to address the question of how noise interferes with animal vocalizations. Seeking the answer with animal experiments is very time consuming, costly, and often infeasible. This article examines the possibility of estimating results with software models. A matched filter, spectrogram cross-correlation, critical band cross-correlation, and a back-propagation neural network detected a beluga vocalization in three types of ocean noise. Performance was compared to masked hearing experiments with a beluga whale [C. Erbe and D. M. Farmer, Deep-Sea Res. II 45, 1373-1388 (1998)]. The artificial neural network simulated the animal data most closely and raised confidence in its ability to predict the interference of a variety of noise source with a variety of vocalizations.
对人为噪声及其对海洋哺乳动物影响的环境评估需要解决噪声如何干扰动物发声的问题。通过动物实验寻找答案非常耗时、成本高昂且往往不可行。本文探讨了使用软件模型估算结果的可能性。匹配滤波器、频谱图互相关、临界带互相关和反向传播神经网络在三种海洋噪声类型中检测到了白鲸的发声。将性能与对白鲸进行的掩蔽听力实验[C. 厄尔贝和D. M. 法默，《深海研究II》45，1373 - 1388(1998)]进行了比较。人工神经网络最接近地模拟了动物数据，并增强了人们对其预测各种噪声源对各种发声干扰能力的信心。
