2020考研英語閱讀理解精讀100篇：Unit 35

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

篇章剖析

本篇文章是一篇說明文，介紹了一款最新發布的“聯機音樂識別和查詢系統”。第一段通過一個生動的例子介紹這種系統的功能；第二段和第三段將這種系統和其他產品的工作原理進行了比較；第四段介紹了這種新產品的音樂分析方法；最后一段介紹了有關音樂版權問題以及這個系統在版權領域所能發揮的作用。

詞匯注釋

query /?kw??ri/ n. 詢問

off-key adj .（唱歌）跑調的

off-track adj . 唱錯曲子的

unveil /??n?ve?l/ v. 使公之于眾

symposium /s?m?p??z??m/ n. （專家、學者的）研討會，專題討論會，座談會

retrieval /r??tri?v?l/ n. 檢索

brainchild /?bre?nt?a?ld/ n. 腦力勞動成果（指計劃、發明等）

text /tekst/ v. 以文本形式發送

musicologist /?mju?z??k?l?d??st/ n. 音樂學者

digitise /?d?d??ta?z/ v. 【計】將資料數字化

wavelet /?we?vl?t/ n. 微（子，弱，小）波

decomposition /?di?k?mp??z???n/ n. 分解

multi-resolution /?m?lti?rez??l???n/ n. 多重分辨率

Fourier analysis 傅立葉分析

polyphase /?p?l?fe?z/ adj. 多相的

filtering /?f?lt?r??/ n. 過濾，濾除

discrete /d?s?kri?t/ adj. 離散的

cosine /?k??sa?n/ n. 【數】余弦

transformation /?tr?nsf??me???n/ n. 變化，轉化

upshot /??p??t/ n. 結果

infringement /?n?fr?nd?m?nt/ n. 侵權

magnum opus /m?gn?m???p?s/ n.〈拉〉巨著

難句突破

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.

主體句式：Worries mean that...

結構分析：這是一個復雜句，句中包含一個that引導的賓語從句，這個從句中有一個詞組gain access to，意思是“可以進入”，此外，句中還有一個由though引導的讓步狀語從句，在這個從句里又有一個if引導的條件狀語從句，而在這個條件狀語從句里又有一個that引導的賓語從句。

句子譯文：出于保護版權的考慮，他們還無法進入各公司的流行音樂資料庫。不過，如果公司意識到回答更多的哼唱問詢就可以帶來更多銷量的話，這種狀況也許會有所改變。

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.

主體句式：There is little point...

結構分析：這是一個復雜句，句子主體結構是一個慣用表達“there is little point in doing something”，意思是“做某事沒有意義”，在這個句子中有一個when引導的時間狀語從句，這個狀語從句的謂語采用了could have done這種虛擬形式，表示“本來能夠做某事而沒做”，另外還有一個動詞詞組set one’s mind at rest，意思是“讓某人放心”，rest后面則是由that引導的同位語從句。此外，主體結構中的動名詞doing也帶了一個自己的賓語從句。

句子譯文：如果在互聯網上快速搜索一下就可以放心地發現自己的大作并沒有抄襲別人作品的痕跡，那么那種托詞也就無法成立了。

題目分析

1. B 主旨題。一般來說，判斷文章主旨要看文章第一段、最后一段以及各段的主題句。文章第一段作者以一個音像店顧客通過哼唱方式查詢想要的音樂可能遇到的困難開始，引出話題，一種新的軟件可能改變這一切。接著在下文里，作者介紹了這種軟件的功能、特點、原理和發展前景等?？v觀全文，這是一篇介紹一種新款軟件的說明文。

2. A 細節題。答案見文章第三段第三至四行。

3. B 語義題。文中第四段介紹了OMRAS進行音樂分析的過程，用first和then連接。經過這兩個階段后就制成了一個聲音的數學模式。根據上下文邏輯，upshot一詞應該是“最后的結果”的意思。

4. D 推理題。文章最后一段提到由于擔心版權問題，OMRAS的研究人員尚且無法訪問公司的流行音樂庫。但是，“如果公司意識到回答更多的哼唱問詢就可以帶來更多銷量的話，這種狀況也許會有所改變”。由此可見，音樂公司還沒有意識到這款軟件的價值。

5. A 情感態度題。通讀全文，作者介紹了OMRAS相比其他產品獨有的優越性能，繼而提到它在防止侵權方面所能起到的作用。最后作者指出，人們只需在互聯網上快速搜索一下就可以放心地發現自己的大作并沒有抄襲別人作品的痕跡。可見，作者對于這種新產品持積極樂觀的態度。

參考譯文

音像店店員的一項日常工作是接受“哼唱問詢”：一位顧客走進店來，把他想要，卻又記不起名稱或者歌手的那首歌哼唱出來。熟悉音樂的店員一般都能說出曲調的名稱，做成一筆交易。不過，哼唱音樂很可能不但跑調而且還搞錯了曲子。因此，很多時候收銀機都是關著的，顧客也只能失望地離去。也許要改變這種狀況只需要一款新軟件。如果“聯機音樂識別和查詢系統”（OMRAS）取得成功的話，那么把記得不太清楚的曲調對著電腦哼唱一遍也許就能找出匹配的音樂。

最近剛在巴黎舉行的“音樂信息檢索國際會議”上被公布的OMRAS是來自倫敦、印第安納和馬薩諸塞等地的大學研究人員共同的智慧結晶。當然，音樂識別軟件早就問世了。舉例來說，手機用戶可以撥打一個叫做“快變”（Shazam）的系統，用手機話筒對準一個音樂源，然后等待樂曲的名稱和演奏/演唱者等信息以文本形式發送到他們的手機上。

“快變”及其類似產品的工作原理都是將聲音和幾百萬首儲存在一個中央數據庫中的錄音資料加以匹配。不過，要讓“快變”匹配成功，音樂源不僅要和已歸檔的錄音相似，而且還必須一致才行。與之相比，OMRAS則對音樂進行分析。這就意味著它可以在同一歌曲的不同演繹風格之間進行匹配。該項目英國小組的負責人馬克·桑德勒說，這一系統當然能夠將業余鋼琴演奏者和職業鋼琴家演奏的同一作品匹配出來。當然它也應該通過“哼唱問詢”測試。

OMRAS的音樂分析方法與音樂學者了解的方法迥然不同。一個樂曲先是被數字化，這樣就可以對它進行處理了。接下來它還要經過一些數學處理程序，比如小波分解、多重分辨率傅立葉分析、多相過濾、離散余弦變換等。最終得出一個聲音的數學模式包含了原始聲音的要素，并排除了風格和質量等干擾因素。接下來就可以把這種聲音要素和聲音庫中已知的各種聲音要素加以比對并進行匹配。不同于“快變”的是，每一個曲調只需要一個聲音庫參考要素。

目前，桑德勒博士和他的同事們的實驗范圍一直被限制在古典音樂模式。他們的數據庫里囊括了巴赫、貝多芬和莫扎特的作品在內的三千多首樂曲。出于保護版權的考慮，他們還無法進入各公司的流行音樂資料庫。不過，如果公司意識到回答更多的哼唱問詢就可以帶來更多銷量的話，這種狀況也許會有所改變。除此之外，OMRAS還能夠幫助防止不經意發生的版權侵犯行為，例如一個作曲家誤把別人的作品當做自己的靈感而出現的剽竊行為。如果換個嘲諷的說法，它甚至還可以防止人們把自己的侵權行為歸結為無心之錯。如果在互聯網上快速搜索一下就可以放心地發現自己的大作并沒有抄襲別人作品的痕跡，那么那種托詞也就無法成立了。