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  3. Hit songs are big business, so there is an incentive for composers to try to tease out those ingredients that might increase the

Hit songs are big business, so there is an incentive for composers to try to tease out those ingredients that might increase the

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问题    Hit songs are big business, so there is an incentive for composers to try to tease out those ingredients that might increase their chances of success. This, however, is hard. Songs are complex mixtures of features. How to analyze them is not obvious and is made more difficult still by the fact that what is popular changes over time. But Natalia Komarova, a mathematician at the University of California, Irvine, thinks she has cracked the problem. As she writes in Royal Society Open Science this week, her computer analysis suggests that the songs currently preferred by consumers are danceable, party-like numbers. Unfortunately, those actually writing songs prefer something else.
   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.
What does "a third lesson" refer to in the last paragraph?
选项 A、A great deal of funding an industry needs to support operation.
B、Measures taken to prevent artists’ privacy from leaking.
C、Money spent for advertising their music.
D、All industries need relevant talents to create value rather than hype.
答案D
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。
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