1.This is an Inside Science story.
这时一则《科学内部》上登载的故事。
2.As the sun rises over the island of American Samoa, a chorus of animal voices drifts upward.
当太阳升到美属萨摩亚岛上空时，动物的声音也随之大声了起来。
3.They're not the calls of birds, though -- the purrs, clicks and groans are coming from under the water.
不过，这不是鸟儿的叫声——而来自水下的咕噜声、咔嗒声和低吟声。
4.New research shows how automation can make it increasingly easy to eavesdrop on the fish making the sounds and uncover how their environment impacts them.
新的研究表明了自动化技术是如何使人们越来越容易偷听到鱼发出的声音，并揭示它们的环境对它们的影响。
5.Jill Munger first heard about fish that make sounds while she was an undergraduate student.
吉尔·芒格在读本科时第一次听说鱼会发出声音。
6.A veteran researcher told her about marine acoustics.
一位资深研究员告诉了她有关海洋声学的事。
7."She described it in this really cool way: I get to spy on critters in the ocean, without disturbing them," said Munger, currently a marine ecology researcher at Oregon State University.
俄勒冈州立大学的海洋生态学研究员芒格说：“她用一种非常酷的方式描述了它：我可以在不打扰它们的情况下监视海洋中的生物。”
8."When you're a diver you disturb the wildlife as you swim through, so you don't get to witness what they are doing when you're not there."
“如果你是潜水员的时候，你会在你游过去的时候打扰野生动物，所以当你不在的时候，你就没法看到它们在做什么。”
9.But passive acoustic monitoring offers an unadulterated soundscape.
但被动声监测提供了一种纯正的声音的状况。
10.Munger was given a hard drive with 18,000 hours of sound from a 12-station hydrophone area maintained by NOAA and the National Park Service in American Samoa -- an amount of data that seemed impossible to sort through, she said.
她说，芒格从NOAA和美属萨摩亚国家公园管理局维护的一个12站水听器区域获得了1.8万小时的声音硬盘，而这一数量的数据似乎无法整理。
11."It's like if someone handed you a weeklong mixtape, you're never going to listen to it all."
“就像有人递给你一张长达一周的录音带，你永远也听不完。”
12.She used software that created a spectrogram -- a visual readout of the noise.
她使用的软件创建了一个声谱图--一种噪音的可视化读数。
13.Some of the sounds were easily identified, such as whale calls.
有些声音很容易识别，比如鲸叫声。
14.But there were other sounds that were totally foreign.
但还有其他完全陌生的声音。
15."It was really almost like a puzzle to figure out what different sounds were," she said.
她说：“搞清楚什么是不同的声音，简直就像一个谜。”
16.Some sections were noisy, like wind, and others like an old scratchy record that was dominated by the sound of snapping shrimp.
有些部分是嘈杂的，像风，还有一些像一张旧的、粗糙的唱片，被捕虾的声音占据了主导地位。
17.Eventually, she determined that they were coming from fish.
最后，她断定这些声音来自于鱼。
18.She was able to pick out the times that fish were calling together -- mornings and nights, like a chorus.
她能够分辨出鱼在一起的时间--早上和晚上，就像合唱一样。
19.There was one sound that excited Munger in particular.
有一个声音让芒格特别兴奋。
20.She is a cat lover, and the damselfish's call reminded her of purring.
她是个爱猫的人，雀鲷鱼的叫声使她想起了猫的呜噜声。
21."To me, [the fish calls] are very distinct and chock-full of personality," she said, adding that damselfish make this particular sound by snapping their pharyngeal teeth together and boosting the sound through their swim bladders.
她说：“对我来说，(鱼叫声)非常独特，充满个性，还补充道，这种特殊的声音是通过咬合咽部牙齿和通过游泳袋增强声音来发出的。
22.The technical term is a pulse train, and it's used in mating.
技术术语脉冲加作序列，也用于生物交配领域。
23.Despite all the intriguing sounds on the hard drive, Munger knew she would never be able to sift through all the data.
尽管硬盘上有很多有趣的声音，但芒格知道她一个人永远无法筛选完所有的数据。
24.So she turned to her brother, Daniel Herrera, a machine learning engineer, for help.
于是，她向她的弟弟丹尼尔·赫雷拉求助，他是一名机器学习工程师。
25.He told her he'd partner with her on the project.
他告诉她自己愿意跟她合伙做这个项目。
26.Herrera wrote the code and together they trained the model.
赫雷拉写代码，然后他们一起搭建了模型。
27.The pair's findings were published in the journal Marine Ecology Progress Series.
他们的发现发表在《海洋生态进展系列》杂志上。
28.The machine learning sample or training data included 400 to 500 damselfish calls.
机器的学习样本或训练数据包括400到500个雀鲷的叫声。
29.With that start, Herrera, a co-author of the paper, built a machine learning model that accurately identified 94% of damselfish calls.
在此基础上，论文的合著者赫雷拉建立了一个机器学习模型，精确地识别了94%的雀鲷的叫声。
30.Research bottlenecks can arise when it's difficult to efficiently analyze vast amounts of data -- but systems like this can be game changers, said Carrie Bell, a researcher at the Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder, who was not involved in the new paper.
科罗拉多州博尔德大学环境科学合作研究所的研究员凯莉·贝尔说，当很难有效地分析大量数据时，就会出现研究瓶颈--但像这样的系统可能会改变游戏规则，她没有参与这篇新论文。
31."When you start to introduce algorithms and approaches like machine learning and artificial intelligence and this deep learning, you're able to build something that is smarter and way more efficient to get through large amounts of data."
“当你开始引入像机器学习和人工智能这样的算法和方法，以及这种深入研究时，你就能够建立一些更聪明、更有效的方法来处理大量的数据。”
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