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(xí)題命題模仿對(duì)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(fā)布的“聯(lián)機(jī)音樂識(shí)別和查詢系統(tǒng)”。第一段通過一個(gè)生動(dòng)的例子介紹這種系統(tǒng)的功能；第二段和第三段將這種系統(tǒng)和其他產(chǎn)品的工作原理進(jìn)行了比較；第四段介紹了這種新產(chǎn)品的音樂分析方法；最后一段介紹了有關(guān)音樂版權(quán)問題以及這個(gè)系統(tǒng)在版權(quán)領(lǐng)域所能發(fā)揮的作用。

詞匯注釋

query /?kw??ri/ n. 詢問

off-key adj .（唱歌）跑調(diào)的

off-track adj . 唱錯(cuò)曲子的

unveil /??n?ve?l/ v. 使公之于眾

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

retrieval /r??tri?v?l/ n. 檢索

brainchild /?bre?nt?a?ld/ n. 腦力勞動(dòng)成果（指計(jì)劃、發(fā)明等）

text /tekst/ v. 以文本形式發(fā)送

musicologist /?mju?z??k?l?d??st/ n. 音樂學(xué)者

digitise /?d?d??ta?z/ v. 【計(jì)】將資料數(shù)字化

wavelet /?we?vl?t/ n. 微（子，弱，?。┎?

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(shù)】余弦

transformation /?tr?nsf??me???n/ n. 變化，轉(zhuǎn)化

upshot /??p??t/ n. 結(jié)果

infringement /?n?fr?nd?m?nt/ n. 侵權(quán)

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(jié)構(gòu)分析：這是一個(gè)復(fù)雜句，句中包含一個(gè)that引導(dǎo)的賓語從句，這個(gè)從句中有一個(gè)詞組gain access to，意思是“可以進(jìn)入”，此外，句中還有一個(gè)由though引導(dǎo)的讓步狀語從句，在這個(gè)從句里又有一個(gè)if引導(dǎo)的條件狀語從句，而在這個(gè)條件狀語從句里又有一個(gè)that引導(dǎo)的賓語從句。

句子譯文：出于保護(hù)版權(quán)的考慮，他們還無法進(jìn)入各公司的流行音樂資料庫(kù)。不過，如果公司意識(shí)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(huì)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(jié)構(gòu)分析：這是一個(gè)復(fù)雜句，句子主體結(jié)構(gòu)是一個(gè)慣用表達(dá)“there is little point in doing something”，意思是“做某事沒有意義”，在這個(gè)句子中有一個(gè)when引導(dǎo)的時(shí)間狀語從句，這個(gè)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(gè)動(dòng)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(dǎo)的同位語從句。此外，主體結(jié)構(gòu)中的動(dòng)名詞doing也帶了一個(gè)自己的賓語從句。

句子譯文：如果在互聯(lián)網(wǎng)上快速搜索一下就可以放心地發(fā)現(xiàn)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(gè)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(diǎn)、原理和發(fā)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(xì)節(jié)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(jìn)行音樂分析的過程，用first和then連接。經(jīng)過這兩個(gè)階段后就制成了一個(gè)聲音的數(shù)學(xué)模式。根據(jù)上下文邏輯，upshot一詞應(yīng)該是“最后的結(jié)果”的意思。

4. D 推理題。文章最后一段提到由于擔(dān)心版權(quán)問題，OMRAS的研究人員尚且無法訪問公司的流行音樂庫(kù)。但是，“如果公司意識(shí)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(huì)有所改變”。由此可見，音樂公司還沒有意識(shí)到這款軟件的價(jià)值。

5. A 情感態(tài)度題。通讀全文，作者介紹了OMRAS相比其他產(chǎn)品獨(dú)有的優(yōu)越性能，繼而提到它在防止侵權(quán)方面所能起到的作用。最后作者指出，人們只需在互聯(lián)網(wǎng)上快速搜索一下就可以放心地發(fā)現(xiàn)自己的大作并沒有抄襲別人作品的痕跡?？梢姡髡邔?duì)于這種新產(chǎn)品持積極樂觀的態(tài)度。

參考譯文

音像店店員的一項(xiàng)日常工作是接受“哼唱問詢”：一位顧客走進(jìn)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(diào)的名稱，做成一筆交易。不過，哼唱音樂很可能不但跑調(diào)而且還搞錯(cuò)了曲子。因此，很多時(shí)候收銀機(jī)都是關(guān)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(lián)機(jī)音樂識(shí)別和查詢系統(tǒng)”（OMRAS）取得成功的話，那么把記得不太清楚的曲調(diào)對(duì)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(guó)際會(huì)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(xué)研究人員共同的智慧結(jié)晶。當(dāng)然，音樂識(shí)別軟件早就問世了。舉例來說，手機(jī)用戶可以撥打一個(gè)叫做“快變”（Shazam）的系統(tǒng)，用手機(jī)話筒對(duì)準(zhǔn)一個(gè)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(fā)送到他們的手機(jī)上。

“快變”及其類似產(chǎn)品的工作原理都是將聲音和幾百萬首儲(chǔ)存在一個(gè)中央數(shù)據(jù)庫(kù)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(duì)音樂進(jìn)行分析。這就意味著它可以在同一歌曲的不同演繹風(fēng)格之間進(jìn)行匹配。該項(xiàng)目英國(guó)小組的負(fù)責(zé)人馬克·桑德勒說，這一系統(tǒng)當(dāng)然能夠?qū)I(yè)余鋼琴演奏者和職業(yè)鋼琴家演奏的同一作品匹配出來。當(dāng)然它也應(yīng)該通過“哼唱問詢”測(cè)試。

OMRAS的音樂分析方法與音樂學(xué)者了解的方法迥然不同。一個(gè)樂曲先是被數(shù)字化，這樣就可以對(duì)它進(jìn)行處理了。接下來它還要經(jīng)過一些數(shù)學(xué)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(gè)聲音的數(shù)學(xué)模式包含了原始聲音的要素，并排除了風(fēng)格和質(zhì)量等干擾因素。接下來就可以把這種聲音要素和聲音庫(kù)中已知的各種聲音要素加以比對(duì)并進(jìn)行匹配。不同于“快變”的是，每一個(gè)曲調(diào)只需要一個(gè)聲音庫(kù)參考要素。

目前，桑德勒博士和他的同事們的實(shí)驗(yàn)范圍一直被限制在古典音樂模式。他們的數(shù)據(jù)庫(kù)里囊括了巴赫、貝多芬和莫扎特的作品在內(nèi)的三千多首樂曲。出于保護(hù)版權(quán)的考慮，他們還無法進(jìn)入各公司的流行音樂資料庫(kù)。不過，如果公司意識(shí)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(huì)有所改變。除此之外，OMRAS還能夠幫助防止不經(jīng)意發(fā)生的版權(quán)侵犯行為，例如一個(gè)作曲家誤把別人的作品當(dāng)做自己的靈感而出現(xiàn)的剽竊行為。如果換個(gè)嘲諷的說法，它甚至還可以防止人們把自己的侵權(quán)行為歸結(jié)為無心之錯(cuò)。如果在互聯(lián)網(wǎng)上快速搜索一下就可以放心地發(fā)現(xiàn)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。