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Researchers Analyzed2 Folk Music like It Was DNA3: They Found Parallels between Life and Art
Using software designed to align4 DNA sequences, scientists cataloged the mutations that arose as folk songs evolved
Karen Hopkin: This is Scientific American’s 60-Second Science. I’m Karen Hopkin.
You’re probably familiar with the concept of evolution. Living things evolve by accumulating genetic5 changes, which are then weeded out or preserved through a process of natural selection.
Turns out the same thing happens in music. And by using the same software that’s used to track mutations in genes6, researchers have mapped out the sorts of changes that shape the evolution of songs. The findings appear in the journal Current Biology. [Patrick E. Savage7 et. al, Sequence alignment8 of folk song melodies reveals cross-cultural regularities9 of musical evolution]
Patrick Savage: I’ve always loved music since I was a child.
Hopkin: Patrick Savage, an ethnomusicologist at Keio University in Fujisawa, Japan.
Savage: I grew up singing English folk songs. My dad really likes folk music and often has his friends come over and do jam sessions at home. Then, when I moved to Japan about 11 years ago, I started studying Japanese folk songs. And I really liked that repertoire10, too.
Hopkin: The style was very different from the music he grew up with.
Savage: So, like [sings tonal sounds].
Hopkin: Yet the way the songs are learned, by trying to imitate a recording11 or a teacher, is pretty much the same.
Savage: So it made sense to test these ideas about “Are these general evolutionary12 rules that we find in music, especially in these folk songs, repertoires13 I know, that would kind of parallel what we find in genetics and allow us to get a more sort of general unifying14 theory about music and evolution across different cultures?”
Hopkin: At first, he and his colleagues hoped to tackle a huge reconstruction15 of the family tree of all folk music.
Savage: But kind of quickly, [we] realized that it was very—it would be quite challenging to do because when you build these phylogenies, these family trees, you kind of have to make a lot of assumptions about how the process works.
Hopkin: So, for example, geneticists know what kinds of mutations crop up in DNA—and with what frequency—information they can then use to assemble and calibrate16 their gene-based phylogenetic trees. But Savage says they didn’t have the same level of knowledge for music.
Savage: So we decided17 that, rather than try to do the big reconstructions18, we would first focus on the simplest case, which is the pairs.
Hopkin: Savage and his team combed through enormous catalogs of English and Japanese folk songs to identify pairs of melodies that were clearly related—like two different versions of the song “Scarborough Fair,” which is actually based on a traditional English ballad19 about an elfin knight20.
[CLIP: Woman sings “Scarborough Fair”]
Savage: With the English ones, people had been going out there and notating things by ear since at least the early 1900s.
Hopkin: And by the mid-1900s, a similar process had begun in Japan.
Savage: They just kind of sent teams of scholars out throughout all of Japan and said, “We need to collect all the folk songs before they disappear.”
Hopkin: So Savage had a pool of some 10,000 tunes21 to work with.
Savage: I just had to go through and just and look at the notations22 in the anthologies and kind of sing them to myself as I converted them into these sequences of text—Cs and Ds and Gs and things like that—so we could run the sequence alignment algorithms on them.
Hopkin: So what did team Savage learn? Well, a few things.
Savage: One was that more functional23 notes, notes that had stronger rhythm functions, would be more stable.
Hopkin: So notes that are key to the melody.
Savage: You listen to “Scarborough Fair,” the end, you know, “She once was a true love of mine.” The final note is a very strong downbeat. And it’s also the last note where you’re kind of always expecting a note. So very rarely would you end on like “She once was a true love of mine.” It feels very unfinished. Likewise, you would never expect that note to just be deleted. You wouldn’t expect “She once was a true love of....” That would just be very strange.
Hopkin: Next, they found that when one note mutates to another note, the changes tend to be small.
Savage: So like one or two semitones above or below where it would have been rather than six or seven semitones. Which would be a difference of like, [sings] “la la” versus24 like [sings] “la la.”
Hopkin: Here, for example, Savage sings snippets of a Japanese lullaby.
Savage: These ones have different lyrics25 but almost the same melody. The first one was notated from the singing of Tonsui Kikuchi. And it sounds something like this [sings].
And the second one, notated from the singing of Shigeri Kitsu, sounds like this [sings].
So the differences there, for example, the last one [sings] versus [sings] are very small, just a semitone difference, but [they are] an example of a small substitution distance.
Hopkin: Such small substitutions have minimal26 effect on the overall melody. So they’re the essentially27 the musical equivalent of what geneticists call a “neutral mutation,” one that doesn’t alter an organism’s fitness.
Now, all that seems pretty straightforward28. But the next finding was a bit of a surprise.
Savage: There’s two different kinds of mutations you can have in genetics or music. The substitutions are one-note changes to another note. Or you can have an insertion or deletion where a note is either inserted or deleted from the sequence or a nucleotide is inserted or deleted from the sequence. In genetics, these are very rare.
Hopkin: That’s because the instructions carried by genes are read in sets of three nucleotides. Add or remove just one, and you throw off the whole register, which messes up the rest of the message.
Savage: But we found, in music, insertions/deletions were actually quite a bit more common than the substitutions.
Hopkin: That’s because they can easily be accommodated by holding other notes longer or singing some faster, leaving the melody intact. So in one version of “Scarborough Fair” ...
Savage: So Martin Carthy kinda sings, “Parsley sa-a-age, rosemary and thyme.” And Simon and Garfunkel just sing “parsley, sage29, rosemary and thyme.” So, this little “sa-a-age” ornament30 is just deleted. But they just sing the “sage” a little bit longer, and it takes up the same amount of rhythmic31 space.
Hopkin: Savage says that many of these mutations, like their genetic counterparts, are probably accidental.
Savage: That’s what I do when I learn songs. I’ll be learning from my singer, and then I’ll record myself singing, and I’ll realize that I’ve sung a couple of notes a little bit different—a little bit higher here, a little bit lower there. Or I added an extra note by accident. I’m usually not consciously trying to change what my teacher has sung. But it’s just easy to crop up.
Hopkin: Using a genetic approach to analyze1 melodies also has some practical applications.
Savage: We can apply these sequence alignment techniques to quantify how similar two songs are and how likely the changes are to happen and sort of have little bit more quantitative32 evidence for these high-profile multimillion-dollar [copyright] cases like “Blurred Lines” or George Harrison’s case with the Chiffons and “My Sweet Lord”/“He’s So Fine.”
Hopkin: At the same time, Savage looks forward to continuing to explore music’s ancestral roots as a scientist and as a musician.
Savage: Everyone’s always inspired by the great musicians of the past. But, like, these currents of evolution go back hundreds of thousands of years. So, yeah, it’s kind of this sort of connection with other humans through music at a very deep level and throughout time is one that kind of excites me as a performer.
Hopkin: And it makes his science sing.
[CLIP: Patrick Savage and Gakuto Chiba sing the same Japanese folk song, “Kuroda Bushi”]
Hopkin: Special thanks to Pat Savage and his student Gakuto Chiba for their vocals33. And a final note on “Scarborough Fair.” The first version you heard came by way of Wikimedia Commons user Makemi. We’ll include a link to that recording in the podcast transcript34. And our bonus, hidden track was sung by Mrs. G. A. Griffith in 1939, recorded by John and Ruby35 Lomax.
Hopkin: For Scientific American’s 60-Second Science, I’m Karen Hopkin
1 analyze | |
vt.分析,解析 (=analyse) | |
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2 analyzed | |
v.分析( analyze的过去式和过去分词 );分解;解释;对…进行心理分析 | |
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3 DNA | |
(缩)deoxyribonucleic acid 脱氧核糖核酸 | |
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4 align | |
vt.使成一线,结盟,调节;vi.成一线,结盟 | |
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5 genetic | |
adj.遗传的,遗传学的 | |
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6 genes | |
n.基因( gene的名词复数 ) | |
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7 savage | |
adj.野蛮的;凶恶的,残暴的;n.未开化的人 | |
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8 alignment | |
n.队列;结盟,联合 | |
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9 regularities | |
规则性( regularity的名词复数 ); 正规; 有规律的事物; 端正 | |
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10 repertoire | |
n.(准备好演出的)节目,保留剧目;(计算机的)指令表,指令系统, <美>(某个人的)全部技能;清单,指令表 | |
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11 recording | |
n.录音,记录 | |
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12 evolutionary | |
adj.进化的;演化的,演变的;[生]进化论的 | |
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13 repertoires | |
全部节目( repertoire的名词复数 ); 演奏曲目 | |
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14 unifying | |
使联合( unify的现在分词 ); 使相同; 使一致; 统一 | |
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15 reconstruction | |
n.重建,再现,复原 | |
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16 calibrate | |
校准;使合标准;测量(枪的)口径 | |
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17 decided | |
adj.决定了的,坚决的;明显的,明确的 | |
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18 reconstructions | |
重建( reconstruction的名词复数 ); 再现; 重建物; 复原物 | |
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19 ballad | |
n.歌谣,民谣,流行爱情歌曲 | |
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20 knight | |
n.骑士,武士;爵士 | |
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21 tunes | |
n.曲调,曲子( tune的名词复数 )v.调音( tune的第三人称单数 );调整;(给收音机、电视等)调谐;使协调 | |
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22 notations | |
记号,标记法( notation的名词复数 ) | |
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23 functional | |
adj.为实用而设计的,具备功能的,起作用的 | |
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24 versus | |
prep.以…为对手,对;与…相比之下 | |
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25 lyrics | |
n.歌词 | |
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26 minimal | |
adj.尽可能少的,最小的 | |
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27 essentially | |
adv.本质上,实质上,基本上 | |
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28 straightforward | |
adj.正直的,坦率的;易懂的,简单的 | |
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29 sage | |
n.圣人,哲人;adj.贤明的,明智的 | |
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30 ornament | |
v.装饰,美化;n.装饰,装饰物 | |
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31 rhythmic | |
adj.有节奏的,有韵律的 | |
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32 quantitative | |
adj.数量的,定量的 | |
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33 vocals | |
(乐曲中的)歌唱部份,声乐部份( vocal的名词复数 ) | |
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34 transcript | |
n.抄本,誊本,副本,肄业证书 | |
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35 ruby | |
n.红宝石,红宝石色 | |
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