《考研英語閱讀理解100篇 基礎版》第3章　信息技術類 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．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. 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．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. 
注（1）：本文選自Economist； 
注（2）：本文習題命題模仿對象：第1題模仿2000年真題Text 3第1題，第2題模仿2001年真題Text 4第2題，第3題模仿2004年真題Text 3第4題，第4題模仿2003年真題Text 1第4題，第5題模仿2002年真題Text 3第5題。 
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