考研英语阅读理解精读100篇【信息技术类】

考研英语阅读理解精读100篇unit35
Unit 35
Assistants in record shops are used to receiving "humming queries": a customer comes into the store humming a song he wants, but cannot remember either the title or the artist. Knowledgeable staff are often able to name that tune and make a sale. Hummers, though, can be both off-key and off-track. Frequently, therefore, the cash register stays closed and the customer goes away disappointed. A new piece of software may change this. If Online Music Recognition and Searching (OMRAS) is successful, it will be possible to hum a half-remembered tune into a computer and get a match.
 
OMRAS, which has just been unveiled at the International Symposium on Music Information Retrieval, in Paris, is the brainchild of a group of researchers from the Universities of London, Indiana and Massachusetts. Music-recognition programs exist already, of course. Mobile-phone users, for instance, can dial into a system called Shazam, hold their phones to a source of music, and then wait for the title and artist to be texted back to them.
 
Shazam and its cousins work by matching sounds directly to recordings, several million of them, stored in a central database. For Shazam to make a match, though, the music source must be not just similar to, but actually identical with, one of the filed recordings. OMRAS, by contrast, analyses the music. That means it can make a match between different interpretations of the same piece. According to Mark Sandler, the leader of the British side of the project, the program would certainly be able to match performances of the same work by an amateur and a professional pianist. It should also pass the humming-query test.
 
The musical analysis performed by OMRAS is unlike any that a musicologist would recognise. A tune is first digitised, so that it can be processed. It is then subject to such mathematical indignities as wavelet decomposition, multi-resolution Fourier analysis, polyphase filtering and discrete cosine transformation. The upshot is a mathematical model of the sound that contains the essence of the original, without such distractions as style and quality. That essence can then be compared with a library of known essences and a match made. Unlike Shazam, only one library reference per tune is needed.
 
So far, Dr Sandler and his colleagues have been restricted to modelling classical music. Their 3,000-strong database includes compositions by Bach, Beethoven and Mozart. Worries about copyright mean that they have not yet gained access to company archives of pop music, though if the companies realise that the consequence of more humming queries being answered is more sales, this may change. On top of that, OMRAS could help to prevent accidental copyright infringements, in which a composer lifts somebody else's work without realising his inspiration is second-hand. Or, more cynically, it will stop people claiming that any infringement was accidental. There is little point in doing that when a quick check on the Internet could have set your mind at rest that your magnum opus really was yours.
 
注（1）：本文选自Economist；10/19/2002, p77, 2/3p, 1c；
注（2）：本文习题命题模仿对象2000年真题text 3第1题（1），2001年真题text 4第2题（2），2004年真题text 3第4题（3）；2003年真题text 1第4题（4），2002年真题text 3第5题（5）；
 
1.       The passage is mainly__________.
 
[A] a comparison of two music-recognition programs
[B] an introduction of a new software
[C] a survey of the music recognition and searching market
[D] an analysis of the functions of music recognition softwares
 
2.       According to the author, one of the distinctive features of OMRAS is________.
 
[A] its ability to analyze music
[B] its large database
[C] its matching speed
[D] its ability to match music of different pieces
 
3.       The word “upshot” (Line 4, Paragraph 4) most probably means_________.
 
[A] last step
[B] final result
[C] goal
[D] program
 
4.       We can learn from the last paragraph that__________.
 
[A] OMRAS will facilitate copyright infringements
[B] OMRAS researchers are fans of classical music
[C] composers can get more inspiration with the help of OMRAS
[D] music companies are yet to realize the value of OMRAS
 
5.       From the text we can see that the writer seems__________.
 
[A] optimistic
[B] uncertain
[C] indifferent
[D] skeptical
 
答案：BABDA
 
篇章剖析
本篇文章是一篇说明文，介绍了一款最新发布的“联机音乐识别和查询系统”。第一段通过一个生动的例子介绍这种系统的功能。第二段和第三段将这种系统和其他的产品的工作原理进行了比较。第四段介绍了这种新产品的音乐分析方法。最后一段介绍了有关音乐版权问题以及这个系统在版权领域所能发挥的作用。
 
词汇注释
query[] n. 询问
off-key（唱歌）跑调
off-track 唱错曲子
unveil[] v. 使公诸于众
symposium[] n. （专家、学者的）研讨会，专题讨论会，座谈会
retrieval[] n. 检索；从内存或其它存储设备中获取信息的过程
brainchild[] n. 脑力劳动成果（指计划、发明等）
text[] v. 以文本形式发送
musicologist[] n. 音乐学者
digitize[] v. [计]将资料数字化
wavelet[] n. 微[子, 弱, 小]波
decomposition[] n. 分解
multi-resolution  n. 多重分辨率
Fourier analysis  傅立叶分析
polyphase[] adj.多相的
filtering[] n. 过滤, 滤除
discrete[] adj. 离散的
cosine[] n. [数]余弦
transformation[] n. 变化, 转化
upshot[] n. 结果
infringement[] n. 侵权侵害一项权利或特权
magnum opus[] n. <拉> 巨著, 杰作, 代表作, 主要作品
 
难句突破
1. Worries about copyright mean that they have not yet gained access to company archives of pop music, though if the companies realise that the consequence of more humming queries being answered is more sales, this may change.
主体句式：worries mean that …
结构分析：这是一个复杂句，句中包含一个that引导的宾语从句，这个从句中有一个词组gain access to，意思是“可以进入”，此外，句中还有一个由though引导的让步状语从句，在这个从句里又有一个if引导的条件状语从句，而在这个条件状语从句里又有一个that引导的宾语从句。
句子译文：对于版权的担忧意味着他们还无法进入各公司的流行音乐资料库，虽然如果公司意识到回答更多的哼唱问询就可以带来更多销量的话，这种状况也许会有所改变。
2. There is little point in doing that when a quick check on the Internet could have set your mind at rest that your magnum opus really was yours.
主体句式：There is little point…
结构分析：这是一个复杂句，句子主体结构是一个惯用表达“there is little point in doing something”，意思是“做某事没有意义”，在这个句中有一个when 引导的时间状语从句，这个状语从句的谓语采用了could have done这种虚拟形式，表示“本来能够做某事”，另外还有一个成语动词词组set one’s mind at rest，意思是“让某人放心”，rest后面则是由that引导的同位语从句。此外，主体结构中的动名词doing也带了一个自己的宾语从句。
句子译文：如果在互联网上快速搜索一下就可以放心地发现自己的大作的确实至名归，那么那种做法也就没有什么意义了。
 
题目分析
1. 答案为B，属主旨大意题。一般来说，判断文章主旨，要看文章第一段，最后一段以及各段的主题句的内容。文章第一段作者以一个音像店顾客通过哼唱方式查询想要的音乐可能遇到的困难开始，引出话题，一种新的软件可能改变这一切。接着在下文里，作者介绍了这种软件的功能，特点，原理，发展前景等。纵观全文，这是一篇介绍这种新款软件的说明文。
2. 答案为A，属事实细节题。答案见文章第二段第四行。
3. 答案为B，属猜词题。文中第4段介绍了OMRAS进行音乐分析的过程。用了first，then连接手段。经过这两个阶段后就制成了一个声音的数学模式。可见根据上下文逻辑，upshot一词应该是“最后的结果”的意思。
4. 答案为D，属推理判断题。文章最后一段提到由于担心版权问题，OMRAS的研究人员尚且无法访问公司的流行音乐库。但是，“如果公司意识到回答更多的哼唱问询就可以带来更多销量的话，这种状况也许会有所改变”。由此可见，音乐公司还没有意识到这款软件的价值。
5. 答案为A，属推理判断题。根据上文，作者介绍了OMRAS的相比其他产品的优越性能，继而提到它在防止侵权方面所能起到的作用。最后说，人们只需在互联网上快速搜索一下就可以放心地发现自己的大作并没有抄袭别人作品的痕迹。可见，作者对于这种新产持积极乐观的态度。
 
参考译文
音像店的店员已经习惯了接受“哼唱问询”：一位顾客走进店来，把他想要，却又记不起名称或者歌手的那首歌哼唱出来。熟悉音乐的店员一般都能说出曲调的名称，做成一笔交易。不过，那些哼唱音乐的人很可能不但跑调而且还搞错了曲子。因此，很多时候收银机都是关着的，顾客也只能失望地离去。一款新软件也许会改变这种状况。如果“联机音乐识别和查询系统”（OMRAS）取得成功的话，那么把记得不太清楚的曲调对着电脑哼唱一遍也许就能找出匹配的音乐。
 
最近刚在巴黎举行的“音乐信息检索国际会议”上被公布的OMRAS是来自伦敦，印第安纳和马萨诸塞等地的大学研究人员的智慧结晶。当然，音乐识别软件早就问世了。举例来说，手机用户可以拨打一个叫做“快变”（Shazam）的系统，用手机话筒对准一个音乐源，然后等待乐曲的名称和演奏/唱者等信息以文本形式发送到他们的手机上。
 
“快变”及其类似产品的工作原理都是将声音和几百万首储存在一个中央数据库中的录音资料加以匹配。不过，要让“快变”匹配成功，音乐源不仅要和已归档的录音相似，而且还必须一致才行。与之相比，OMRAS则对音乐进行分析。这就意味着它可以在同一歌曲的不用演绎风格之间进行匹配。该项目英国小组的负责人马克•桑德勒说，这一系统当然能够将业余钢琴演奏者和职业钢琴家演奏的同一作品匹配出来。它也应该通过“哼唱问询”测试。
 
OMRAS的音乐分析方法不同于音乐学者了解的任何方法。一个乐曲先是被数字化，这样就可以对它进行处理了。接下来它还要经过一些数学处理程序，比如小波分解，多重分辨率傅立叶分析，多相过滤，离散余弦变换等。最终得出一个声音的数学模式，它包含了原始声音的要素，并排除了风格，质量等干扰因素。接下来就可以把这种声音要素和声音库中已知的各种声音要素加以比对并进行匹配。不同于“快变”的是，每一个曲调只需要一个声音库参考要素。
 
目前，桑德勒博士和他的同事们一直被限制在制作古典音乐模式的范围内。他们的数据库里有包括巴赫，贝多芬和莫扎特的作品在内的三千多首乐曲。对于版权的担忧意味着他们还无法进入各公司的流行音乐资料库，虽然如果公司意识到回答更多的哼唱问询就可以带来更多销量的话，这种状况也许会有所改变。除此之外，OMRAS还能够帮助防止不经意发生的侵犯版权行为，例如一个作曲家误把别人的作品当作自己的灵感而出现的剽窃行为。更具嘲讽意味的是，它甚至还可以防止人们宣称自己的侵权行为是无心之错。如果在互联网上快速搜索一下就可以放心地发现自己的大作的确实至名归，那么那种做法也就没有什么意义了。

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