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Passage

A The versificator, a machine described in George Orwell’s novel “1984”, automatically generated music for the hapless masses. The idea of removing humans from the creative process of making music, an art form so able to stir the soul, made for a good joke when the book was published in 1949. But today, computer programmers working in a new field called “music intelligence” are developing software capable of predicting which songs will become hits. This surprisingly accurate technology could profoundly change the way pop music is created.

B The software uses a process called “spectral deconvolution” to isolate and analyse around 30 parameters that define a piece of music, including such things as sonic brilliance, octave, cadence, frequency range, fullness of sound, chord progression, timbre and “bend” (variations in pitch at the beginning and end of the same note). “Songs conform to a limited number of mathematical equations,” says Mike McCready of Platinum Blue, a music-intelligence company based in New York, that he founded last December. Platinum Blue has compiled a database of more than 3m successful musical arrangements, including data on their popularity in different markets. To the human ear, music has changed a lot over the years. Music-intelligence software, however, can reveal striking similarities in the underlying parameters of two songs from different eras that, even to a trained ear, seem unrelated. According to Platinum Blue’s software, called Music Science, for example, a number of hit songs by U2 have a close kinship to some of Beethoven’ s compositions. If a song written today has parameters similar to those of a number of past hits, it could well be a hit too.

C Carlos Quintero, a producer and remixer at Orixa Producciones in Madrid, recently tried out another music-intelligence system, called Hit Song Science (HSS). “It practically left me in shock, it’s stunning,” he says. Mr Quintero’s production company now has the most promising demo songs it receives from aspiring musicians evaluated by Polyphonic HMI, the Barcelona- based developer of HSS and Platinum Blue’s only serious competitor. (Both companies perform analyses in-house, rather than selling software.) The results—consisting of a graph, numerical scores, computer-generated comments and suggested changes—help Orixa’s managers decide which songs to produce. Then, during the recording and post-production phases, Orixa uses HSS to reanalyse successive versions of each track for fine-tuning.

D Belief in music intelligence is spreading, as Polyphonic HMI and Platinum Blue rack up bull’s-eye predictions of success, including “Candy Shop” by 50 Cent, “Be the Girl” by Aslyn, “Unwritten” by Natasha Bedingfield, “She Says” by Howie Day, and “You’re Beautiful” by James Blunt. Still, labels that use music intelligence generally prefer to keep quiet about it, so non-disclosure agreements are common. “No one wants people to think their decisions are coming from a box,” says Ric Wake, an American producer of two Grammywinning acts who routinely employs Music Science. Even so, the names of many customers have leaked out. They include Capitol Records, Universal Music Group, Sony Music, EMIand Casablanca Records. Labels sometimes don’t tell even their established artists when they use music intelligence to help decide which singles to promote.

E Revenues at Polyphonic HMI will exceed $lm this year, twice last year’s take. In March the company began serving India’s music industry, after compiling a database of that country’s pop music. Platinum Blue refuses to release figures. But one of its managers, Trade Reed (who, like several people at Platinum Blue, used to work at Polyphonic HMI), says customers now come knocking— a reversal of the state of affairs not long ago, when “people’s eyes glazed over and they asked things like, ’Are you joking?”’ The service is relatively inexpensive: a year’s subscription for unlimited analyses typically costs a large record company around $100,000. And the service reduces the need for expensive “call-out” research, in which labels call consumers, play part of a song over the telephone, and compile their reactions.

F It is not just record companies that are interested in music intelligence, however. The market is expanding as radio playlist-programmers adopt the technology, often to put mathematically similar songs together to create a better “flow”. Mobile operators such as Vodafone and Orange use the technology to develop mobile ring tones. Disney’s Hollywood Records uses music intelligence to design soundtracks. Mr McCready of Platinum Blue says television advertising agencies have expressed interest in using it to select jingles, which, while structurally similar to those in a successful previous campaign, sound fresh to consumers. Lawyers are also interested in using the technology. Hillel Parness, a specialist in music copyright-violation at Brown Raysman, a law firm in New York, contacted Platinum Blue to discuss the legal applications of the software. He would like to use the software in plagiarism suits as an objective way to alert judges, who often have little background in music, to suspicious similarities between two pieces of music. Musicintelligence software could also rustle up additional (and lucrative) copyright suits. Using a function known as “melody detection”, record labels will soon be able to use the software to find songs that may have plagiarised songs in the label’s catalogue.

G Is there not a danger, however, that giving software a say in music selection will promote uniformity and hamper creativity? The opposite is more likely. High music-intelligence scores can help convince notoriously risk-averse and “it’s-who-you-know” record labels to take a chance on new talent. Take the case of Frederic Monneron, a publisher of equestrian books in Mesnil-Simon, a village of 150 people in Lower Normandy, France. After a setback in his love life, the 43-year-old self-taught guitarist and pianist set up a makeshift home studio, where he wrote and recorded 12 syrupy, and somewhat improbable, romantic-political ballads. For fun, he paid Polyphonic HMIto analyse his songs. The results indicated that the tunes had what it takes. In September a French label will begin distributing 200,000 copies of Monneron’s CD, “Fred’s Pentagone”, in Europe and North America. Two music videos and a tour will follow. “What happened is a fairy tale,” says Mr Monneron.

Questions

Questions 14-19 The reading Passage has seven paragraphs A-G. Which paragraph contains the following information? Write the correct letter A-G, in boxes 14-19 on your answer sheet.

14 Small amount of money cost for record company

15 Working principle of music intelligence

16 Technology Contrasted between past and present

17 Another version of software depicted

18 More singers believe music intelligence

19 Offer opportunities for new talent

Questions 20-25 Complete the following summary of the paragraphs of Reading Passage, using no more than two words from the Reading Passage for each answer. Write your answers in boxes 20-26 on your answer sheet.

Summary

Music intelligence software working theory is using a procedure named 20 ……………… which assess decades of parameters of a music. According to McCready, “Songs follows several 21 ……………… The company he worked in called 22 ……………… which accumulates enough musical database. Music intelligence has ability to distinguish remarkable 23 ……………… between two different songs. For example, a software called 24 ……………… once compared pop songs from U2 and 25 ……………… , and found there were a few close relationship between the two.

Questions 26

Which one is CORRECT statement according to paragraph F and G?

A Music intelligence is not a promising industry

B Music intelligence help judge make right decision

C Music industry dominates music intelligence application

D Music intelligence has a wide range of application

Answers

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