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Dr Komarova and her colleagues collected information on music released in Britain between 1985 and 2015. They looked in public repositories of music “metadata” that are used by music lovers and are often tapped into by academics. They compared what they found in these repositories with what had made it into the charts.
Metadata are information about the nature of a song that can give listeners an idea of what that song is like before they hear it. The repositories presented Dr Komarova and her team with more than 500,000 songs that had been tagged by algorithms which had been trained to detect numerous musical features. The tags included a dozen binary variables (dark or bright timbre; can or cannot be danced to; vocal or instrumental; sung by a man or a woman; and so on). The team fed all of this information into a computer and compared the features of songs that had made it into the charts (roughly 4% of those in the repositories)with those of songs that had not.
Overall, the team's results suggested that songs tagged as happy and bright have become rarer during the past 30 years; the opposites have therefore appeared with greater frequency. That was not, however, reflected in what made it into the charts. Chart successes were happier and brighter (though also less relaxed), than the average songs released during the same year. Chart toppers were also more likely than average songs to have been performed by women. All this is important information for executives of music companies.
Dr Komarova used these results to train her computer to try to predict whether a randomly presented song was likely to have been a hit in a given year. The machine correctly predicted success 75% of the time, compared with the 4% rate that guessing success at random from the music database would yield something else music executives might pay attention to.
Content is not everything. As might be expected, circumstances particularly any fame already attaching to a recording artist or artists had an effect, too. But not a huge one. Adding in information about who was performing a song increased the accuracy of prediction to 85%. That suggests that musical fame is actually attached to talent，rather than to hype. And this, perhaps, is a third lesson for an industry that some believe is not wedded to talent enough.
单选题第1题Which of the following adjectives best describes the nature of songs?
A.Complicated.
B.Ordinary.
C.Splendid.
D.Simple.
参考答案：A
答案解析：该题定位至第1段。本题考查歌曲的特征可以用哪个形容词来描述。由该段第三句“歌曲是具有复杂特征的混合体”，可见歌曲本质上较为复杂，因此A “复杂的”为正确答案。B项“普通的”、C项“美妙的”、D项“简单的”均与句意不符。答案为A。
单选题第2题Which of the following is NOT referred as a difficult reason of analyzing songs for composers?
A.Songs are complex mixtures of features.
B.There isn't a mature method to analyze songs.
C.Music companies control the main resources.
D.Popular changes make it more difficult.
参考答案：C
答案解析：该题定位至第1段。该段主要讲了作曲家要分析歌曲所面临的困难。A项指“歌曲是具有复杂特征的混合体”；B项指“如何分析它们并没有明确的方法”；D项指“流行趋势在不断地变化”；只有C项“音乐公司控制了主要的资源”在文中并未提及，因此答案为C项。
单选题第3题What is“Metadata” mentioned in Paragraph 3?
A.Metadata are information of a song5s nature that can let listeners know the song while they are listening to it.
B.Metadata are information of a song's nature that can let listeners know the song after they hear it.
C.Metadata are information of a song^ nature that can let listeners know about the song in advance.
D.Metadata are information of a song^ nature that can let listens know which song can be popular.
参考答案：C
答案解析：根据“Metadata”定位至第3段。该段第一句对“Metadata”做了介绍：“元数据是关于歌曲性质的信息，可以让听众在听到之前知道该歌曲是什么样的。” A项“在听歌曲的过程中”、B项“听完歌曲后”、D项“了解到哪些歌可能流行起来”均与原意不符，因此答案为C项。
单选题第4题What didn't Dr. Komarova and her colleagues do?
A.They collected information on music released in Britain between 1985 and 2015.
B.They looked in public repositories of music and visited some famous composers.
C.They compared the features of songs that had made it into the charts with those of songs that had not.
D.They looked in public repositories of music “metadata”.
参考答案：B
答案解析：该题需结合第2、3段进行分析。题目问Dr. Komarova和她的同事没有做什么事情。根据2、3段，可知“他们收集了 1985年至2015年间在英国发布的音乐的信息，调查了经常被音乐爱好者使用和学者们研究的音乐‘元数据'公共存储库，将这些存储库中的音乐与上榜音乐进行了比较”，因此A、C、D项均与原文相符。B项中提到的“拜访著 名作曲家”在文中并未提及，因此正确答案为B项。
单选题第5题The underlined word “hit” in Paragraph 5 is closest in meaning to______.
A.blow
B.strike
C.success
D.failure
参考答案：C
答案解析：第5段第一句话的意思是：Komarova博士将这些结果与其电脑对应，试图预测一首随机出现的歌曲是否有可能在某一年冲上热门榜单。在此句中，hit此处表示“大获成功的歌曲”，而不是A项“打击”、B项“罢工”、D项“失败”，因此答案为C项。
单选题第6题What did the team's results suggest?
A.The songs marked as bright have become less frequent.
B.The songs marked as dark have become less frequent.
C.The songs marked as bright have become less frequent in what made it into the charts.
D.The songs marked as dark have become more frequent in what made it into the charts.
参考答案：A
答案解析：该题定位至第4段。第4段主要讲了调查的结果。该团队的结果表明，“在过去30年中，标记为快乐和愉悦的歌曲变得越来越少，相反，其对立面出现的频率更高。然而，这并没有在销量榜单中反映出来”。因此正确答案应为A项。B、C、D三项皆表述有误。
单选题第7题Content is not everything because______.
A.music companies decide the mainstream culture of songs
B.artists5 interest and high demand of salary influence the charts
C.circumstances, especially any fame already attaching to recording artists had an effect
D.artists' fame had an extremely huge effect on songs
参考答案：C
答案解析：该题定位至第6段。题目问内容并非一切的原因是什么。该段二、三句主要讲述了对音乐能否成功也具有影响的因素：“环境尤其是那些己经依附于唱片艺术家或个人艺术家的名声也对歌曲产生了影响，但影响不是很大。”第五句还指出“音乐名气实际上是与才华挂钩的，而不是炒作”。选项A、B在文中并未提及，D项夸大了名声的作用，因此答案为C项。
单选题第8题What does “a third lesson” refer to in the last paragraph?
A.A great deal of funding an industry needs to support operation.
B.Measures taken to prevent artists5 privacy from leaking.
C.Money spent for advertising their music.
D.All industries need relevant talents to create value rather than hype.
参考答案：D
答案解析：该题定位至第6段。题目问文中对于企业来说第三个经验指什么。这道题需要结合整个段落来理解，该段第一句指出“内容并非一切”是经验一；第二、三句指出“环境，尤其是艺术家的名声也对歌曲产生影响，但影响并不大”，是经验二；第四、五句中说“添加歌手信息会将预测的准确性提高到85%,这表明音乐的名气实际上与歌手的才华有关，而不是炒作”，是经验三，也就是说这个行业中歌手的才华是需要注意的第三点。A、B项文中并未提到，C项“打广告”实际上就是炒作，与第五句不符。所以正确答案为D项。
单选题第9题In which section can we find this article in a magazine?
A.International view.
B.Business.
C.Science and technology.
D.Books and arts.
参考答案：C
答案解析：该题的解答需结合全文来看。题目问我们可以从杂志的哪个栏目中找到这篇文章。这篇文章讲的是Komarova博士及其同事针对歌曲的成分做的一项调查，给我们介绍了这项研究的初衷、如何收集数据、研究的方法以及最后得出的结论。总体来看，这是一项科学的研究，因此它应该在科技专栏，答案为C项。
单选题第10题After learning the results of this study, what will music executives do?
A.They will use music database to predicate hit songs.
B.They will do a relevant research on their own.
C.They will recruit some famous composers and singers.
D.They will use machine to predicate hit songs.
参考答案：D
答案解析：第5段中提到，“机器预测的成功率为75%，相比之下，从音乐数据库中随机猜测成功的概率是4%,音乐公司的高管可能会注意到这一点”。由此可判断，A项错误，D项正确，B项和C项文中未提及。答案为D。
涉及考点
2020翻译三级综合能力考试大纲
二、阅读理解
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