합성이론 8장 : Envelopes 살펴보기

합성이론 8: Envelopes 살펴보기

많은 신디사이저들이 사실상 ‘합성(synthesize)’을 하지 않고 있다는 사실을 알고 계신가요? 이들 중 많은 것들은 단순하게 contour generators, envelope, transient generators등만을 사용하고 있기도 합니다. 많은 아날로그 신스들이 지난 시간에 이야기 했듯이 ADSR contour generators을 사용하구 있음은 의심의 여지가 없지요. 하지만 여러분 ADSR contour generators만을 사용하여 만들 수 없는 소리는 수도 없이 많다는 사실도 알아야 합니다. 그렇다면 또 무엇이 필요한지 살펴보지요~.

1. Bold & Brassy
확 내뿜는듯한 brass sound를 만든다고 생각해봅시다. 우리 귀를 사용했을 때, 우리는 이 소리가 고요한가운데에서 (silence) 시작된다고 알고 있으며 음이 시작될 때에 ‘spit’음과 같은 것을 만들어 낸다고 알고 있지요. 그리고 그 소리는 아주 낮은 레벨로 떨어지고 고용한 음색이 온후에 바로 아주 큰 볼륨으로 천천히 치켜 올라갑니다. 그리고 음표는 빠르게 쇠퇴하고 연주자는 부는 것을 멈춥니다. 볼륨과 음의 밝기는 이때에 그림 1과 같이 그려질 것입니다.


자 지금 일반적인 4-단계의 ADSR envelopes 가 전통적인 방식에 의해 보여진 것입니다.(그림2) 그렇다면 왜 우리는 전통적인 이 방식으로는  brass sound를 제대로 만들지 못하는 걸까요? 그 이유는 단순한 4단계의 ‘4’라는 숫자의 한계점뿐만 아니라, 그것이 우리에게 주는 많은 다른 한계점을 지니기 때문입니다. 무슨 한계점이냐고요?

1.        시작이 언제나 0이다.
2.        윤곽의 attack phase가 언제나 상승한다.
3.        ADSR은 언제나 어택 이후에 최대값의 레벨까지 상승한다.
4.        decay는 언제나 어택에 의존하여 하향된다.
5.        sustain level은 언제나 decay의 끝에 도달하는 그 지점에서 시작한다.
6.        sustain level은 지속적인 볼륨만을 취한다.
7.        Release는 sustain의 끝에서 시작한다.
8.        release는 언제나 하향한다.
9.        Release는 언제나 0에서 끝난다.
맨 처음 brass사운드를 만들 때라면 3번과 5번의 문제점이 가장 큰 문제가 되겠지요.

2. Classic으로 돌아가보자
지난챕터를 잘 읽어보셨다면 어려움 없이 잘 보실 수 있으시리라 생각합니다. modular synth 에서Control Voltage mixer를 사용하여 여러 단순한 simple envelope generators를 사용하여 더 복잡한multi-stage envelopes을 만들 수 있고, 예를 들어 이런 것들이 처음과 같은 spit brass contour를 만들어 줄 것입니다. 그러나analogue modular에서는 쓸모 있는 polyphonic sound에서는 어려움을 겪게 될 것입니다. 하지만 걱정하지 마세요. 많은 non-modular synths를 사용할 수 있고 그것이 ADSR generators 이외에 것을 가능하게 해 줍니다. (예를 들어 Korg MS20와 같은). 이것은2개의contour generators를 가지며, 그 중 하나는 5단계, 그리고 하나는 3단계를 가집니다. (DAR envelope: Delay, Attack and Release, 그림 3).

이것은 보시다시피 노트 하나를 시작한 후에, 그리고 contour가 시작되기 전 시간의 길이를 결정하는 delay를 program 해 줍니다. (딜레이 된 vibrato).
5단계의 ADSHR에서는 ‘H’를 제공하는데 이것은 Hold입니다. 그림4 먼저 보세요.

어라…라고 말씀하고 계시죠? Gate가 열린 이후에 한동안 sustain되어있지요. 이것은 일반적으로는 여러분이 키를 놓은 순간 이후에 그 노트가 hold되는 데에 사용됩니다. 따라서 손이 다음 키를 누를 수 있는 시간을 주지요.
또 다른 예를 보지요. 그림 5를 보세요

이것은Initial Level control, 이라 하며 이것은 Attack Level control이기도 합니다. 더 편이한 Attack, Decay, Release times을 제공해 줍니다. (see Figure 5 above). 보기에 ADSR과 비슷하면서도, 더 중요한 차이점을 가지지요. 예를 들어 Initial Level 과 Release의 끝부분이 최소값이 아니라는 것이지요.또한 Sustain 단계가 지정되어있지 않습니다. Decay 는 지수곡선으로 0까지 이릅니다. 멋있지 않나요?!!

3. From Dinosaurs To Digits
우리가 이야기한 모든 악기들은 knobs또는 sliders를 이용하여 내분의 저항을 바꾸어 회로의 응답을 컨트롤 한 것입니다. 이 응답이라는 것은 아마도 예를 들어.. oscil의pitches, 필터의 cutoff frequencies 또는 contour generators의 time constants등등이 되겠지요
그러나 많은 이런 합성은 마이크로프로세서가 많은 메모리와 합성 기능 등을 제어하기 위하여 사용되는 analogue/digital hybrids 입니다. 무슨 말이냐고요? 쉽게 설명하자면 그것들이 사용하고 있는 것은 analogue-to-digital converters 들이라는 것이죠. (아하!) 따라서 voltages를 숫자로 컨트롤하고 이 숫자들은 다시 voltages 로 바뀌어서 소리를 만들지요. 하지만 이것이 메모리에 저장될 때에는 또다시numeric form 입니다.
아시다시피 microprocessors handle numbers 들은 0과 1의 binary지요. 우리는 이것은 ‘bits’라고 하죠. 그리고 이것을 얼마나 사용하느냐가 바로 어떤 주어진 파라매터를 사용할 때에 ‘정밀도’를 결정합니다. 많은 혼합된 합성 hybrid synths 는 5개의 bits를 사용하여 중요한 값을 나타냅니다. 따라서 32 개의 가능한 값을 가지는 것이지요. 더 좋은 것은7 bits를 사용하여128개의 값을 가지게 해 줍니다. 여기에는 한계가 있지요. 옛날에 사용되던 메모리 칩은 아주 비쌌기 때문에 제조업자들이 최소한 적은 buts를 사용하려고 했었던 때가 있었더랬지요. 뭐 부수적인 이야기였습니다.

4. 그래서 무엇이 중요하냐면요

순수한 아날로그에서 디지털로 제어되는 아날로그 구조물을 만드는 것은 이처럼 많은 변수들을 거칩니다. 이런 비싼 control panels 에서 자유로워진 이후에 제조업자들은 신디사이저에 더 많은 기능들을 부가할 수 있었지요. 예를 들어 그림 6에서는 5개 단계를 제어할수 있는 3개 이상의 contour generators 를 보여줍니다.
그림 7에서는  Roland Alpha Juno series에서 제공된 contour generator입니다. 이것은 그림처럼4time(4단계)세팅, 3개 이상의 level을 제공합니다.

그림8 은 EX800에서 5단계의 contour 입니다. 이것은ADSR 이상의 기능을 가지는데, 그 이유는 Break Point 에서 두 개의 level설정이 가능하기 때문입니다. 그러나 여기에서 ‘L1’ parameter를 그냥 지나치게 되지요. 이것은 어택의 끝에서 최대값으로 이르게 되는 바로 그 지점인데요. 이것을 줄이기 위해서 사용된 것이 바로 그림 9입니다. 자. 맨 처음으로 돌아왔지요?
이 장황한 이야기를 하는 이유는 아주 단순한 결론 때문입니다.

Contour generator의 복잡한 구조를 만든다면 더 세부적인 소리의 구조물을 만들 수 있다는 것!
. Think about the type of sounds you want to generate, and choose your instrument carefully so that you can produce them ! (Gordon Reid).

Play It Again, Vladimir (via Computer)

By ANNE MIDGETTE

THE house lights dimmed at the BTI Center for the Performing Arts in Raleigh, N.C., one night last month, the stage lights came up on the grand piano, and in front of a rapt audience Alfred Cortot played Chopin’s Prelude in G (Op. 28, No. 3), as he had not for nearly 80 years.

Cortot is dead, of course. He was not present in physical form, nor was anyone else sitting at the keyboard of the Yamaha Disklavier Pro as the keys rose and fell. But this was his performance come back to life: his gentle touch, his luminosity, even his mistakes, like the light brush of an extra note at the periphery of the final chord.

So, at least, claimed Dr. John Q. Walker, the president of Zenph Studios in Raleigh, which sponsored the event and created the software that allowed Cortot to return. Dr. Walker is developing technology that enables him to break down the sounds of an old recording, digitize them and reproduce them on a Disklavier, an up-to-the-minute player piano that can record and replay performances by means of a CD in a slot above the keyboard. Sophisticated fiber optics control the instrument’s hammers.

Old recordings of great performers are often marred by scratches and surface noise, or by sound badly filtered through primitive microphones. Dr. Walker is offering the same music with the immediacy of live performance and the acoustical advantages of a contemporary piano. To demonstrate the contrast, Dr. Walker also let the audience at the BTI Center hear the original Cortot recording from 1926, which sounds as if sand had been poured on the old disc’s shellac.

“The farther you get from the recordings, the worse they sound,” Dr. Walker said by phone a few days before the concert. “The fundamental root of the problem is that I don’t want to hear a recording. I want to hear the young Horowitz, Schnabel, Fats Waller, Thelonious Monk on an in-tune piano.”

If the claims he is making for his new technology are accurate, he will soon be able to. His plan is to approach the major labels with his software and delve into their back catalogs, acting as a record producer to make old recordings new. Josef Hoffman without the scratches, Glenn Gould without the mumbling: brought back to life and performing on modern pianos, recorded with modern technology.

“People say this is like colorizing old photographs, but it’s not,” Dr. Walker said. “This process is like being able to set up the entire scene of that photograph again and shoot it with a new camera from any angle, forever.”

This is the new world of computer music. In its infancy, way back in the 1960’s, the goal was to use digital technology to create new sounds and new musical forms. Today scientists around the world are turning computers on human performance, seeking to quantify an element once thought to be intangible: the expressivity of a human artist.

The piano is a good place to start. It offers a relatively limited set of variables. With the violin, every aspect of sound production is subject to human vagaries: bow pressure, bow speed, the placement of the fingers. On the piano, it comes down to hammers hitting strings.

Developed by Wayne Stahnke, the first Disklaviers were made in the 1980’s by Bösendorfer, the renowned Viennese piano manufacturer. When that company stopped making them, Yamaha took up the baton, hiring Mr. Stahnke as a consultant. Mr. Stahnke’s best-known Disklavier project was a foretaste of Dr. Walker’s efforts: translations of piano rolls recorded by Sergei Rachmaninoff. The two resulting CD’s of “new” Rachmaninoff performances, both called “A Window in Time” and released in 1998 and 1999, are still available from Telarc. Some listeners find these revelatory. Some find them mechanical, even soulless. The reactions demonstrate a basic difficulty with mechanical reproduction of music: there is a subjective element involved in determining if it works. The final criterion for any such reproduction is the rather imprecise “Turing test” of artificial intelligence: that is, whether it can make the listener think he or she is hearing a person rather than a machine.

At the Austrian Research Institute for Artificial Intelligence, a group of leading researchers known as the Machine Learning, Data Mining and Intelligent Music Processing Group are trying to pinpoint just what it is that fools the ear. Led by Gerhard Widmer, they are looking at everything from improving the way computers “hear” music to isolating the elements of individual performance style, as well as creating graphs and animations to illustrate different pianists’ interpretations of the same passage of music.

In a 2003 paper, “In Search of the Horowitz Factor,” Dr. Widmer and his team described giving the computer 13 recordings of Mozart piano sonatas, played into a Bösendorfer Disklavier by the pianist Roland Batik, to see if they could use the computer to determine rules that described the pianist’s interpretive choices.

They did get some rules, though it turned out that many of them applied equally well to other performances of other music. But the machine generated its own performance of a Mozart sonata movement that it had not heard Mr. Batik play, but based on what it had learned of his style. With this, it took second prize in the International Computer Piano Performance Rendering Contest in Tokyo in 2002. With no stage fright.

“The first question was, can we hear Glenn Gould play again?” Dr. Walker said. “The next question: Cool, can we hear him play other stuff?” To this, Dr. Widmer might answer: We’re getting there.

But there’s still the thorny matter of how to get data from an audio recording into the computer. It’s a question not just of having the computer play back a CD, but of translating the music into a language the computer can understand.

A computer, by itself, can’t recognize the difference between a note of music and a cough. It can’t pick out a melody from a dense weave of counterpoint. It can’t tap its foot to follow a beat – not, at least, in classical music, where the tempos are constantly changing. The first problem Dr. Walker faced was how to get the computer to create a kind of score from the clusters of sounds in a recording.

“A recording is sound waves that were sampled by a microphone,” he said. “We feed those into the computer and try to discover what the notes are. The computer model is a three-dimensional thing: middle C struck in a certain way looks like a 3-D mountain range. We have a model that looks like math equations, and we try to fit to it: Yeah, this looks like it’s a note.”

Dr. Walker – a trained pianist with a degree in software engineering who sold his company a few years ago, creating the time and financial flexibility to work on this project – is coming up with his own answers. But the process is still extremely time-consuming. He is reluctant to say just how slow it is, but he has been working for more than three years, and his demo CD includes only a few tracks: the Cortot, Glenn Gould’s performance of the Aria and first variation of Bach’s “Goldberg” Variations, and part of a track by Art Tatum.

Even after he gets a model that works, Dr. Walker has to contend with the question of reproduction on a Disklavier: can it mimic human performance down to the last detail? Dr. Werner Goebl, a member of Dr. Widmer’s team in Vienna, addressed this as co-author of a paper called “Are Computer-Controlled Pianos a Reliable Tool in Music Performance Research? Recording and Reproduction Precision of a Yamaha Disklavier Grand Piano.” Precisely measuring the Disklavier’s ability to replicate human touch, Dr. Goebl answered his own question: No.

Less high-tech but just as relevant are the variations from one piano to another. A skilled musician compensates for changes in a room or an instrument. A CD cannot. Dr. Walker encountered one aspect of the problem when he took his technology to the Yamaha studios to play his Cortot performance for Mei-Ting Sun, a young concert pianist and the winner of the first Piano-e-Competition in 2002 (judged, in part, via a Disklavier in Japan, which reproduced performances thousands of miles away for one of the judges).

It had to do with the final chord in the Chopin prelude – or, rather, with the extra, wrong note.

“Their piano wasn’t calibrating as ours was,” Dr. Walker said, “and the note didn’t sound. Mei-Ting said: ‘I know this recording. This wasn’t accurate, because Cortot misses the last chord.’ I played it again, and he watched the keyboard and saw that the key went down but didn’t sound. He said, ‘O.K., you guys got it.’ “

Mr. Sun was so convinced that at the North Carolina concert where Dr. Walker’s version of Cortot made his debut, he appeared as the featured live artist: Cortot played a piece, Glenn Gould played a piece, and Mr. Sun played the rest of the evening. He had to; Dr. Walker didn’t have enough music to fill a whole recital.

The technology, in short, is still in its infancy. But Dr. Walker is animated by his vision of the future. Like other scientists – including Dr. Goebl in Vienna, another serious classical musician – he envisions a future of interactive recordings. “We’ve been trained that a recording is a frozen document,” he said. “Why can’t it be like a video game – every time you hear a recorded performance it’s different?” But at the moment, his focus is on making new recordings in a more conventional manner.

Dr. Goebl, in Vienna, supports Dr. Walker’s work and is interested in it. But he questions whether it’s a “real” performance. (Dr. Walker is well aware of such skepticism; his response is simply that you can’t judge until you’ve heard it.) “The timing you can probably get quite right,” Dr. Goebl said. “What is really difficult is to get how long the notes were held and how the pedal was moved and so on. You don’t have that information. You can just guess. The result is something that sounds like but never truly will be Gould. It’s always an approximation.”

So is he saying that Dr. Walker’s track isn’t authentic?

“There you have to go into the philosophical domain,” Dr. Goebl replied. “A recording is just an acoustic document of what took place.”

In other words, a recording isn’t authentic, either. It is also at a remove, or two or three, from the original performer, and it is also affected by the decisions of the engineers who helped create it.

The Gould recording, after all, wasn’t recorded in one take. Many different takes were spliced together to create it. Is it any more real than a computer replica? Only if you say it is.

Musical Tastes Get High-Tech Analysis

By ALEX VEIGA, AP Business Writer
Sun Jun 5,12:10 PM ET

OAKLAND, Calif. – Music retailers are turning to high-tech firms that combine computer analysis with the art of listening to come up with new music suggestions for consumers based on what they already like.

In a computer-crammed space at Savage Beast Technologies, divergent melodies seep softly from headphones worn by young men and women who listen to music with the intensity of submarine sonar operators.

Their job is to discern and define attributes in tunes by artists as diverse as teen diva Hilary Duff and jazz legend Miles Davis.

The listeners classify hundreds of characteristics about each song, including beat, melody, lyrics, tonal palette and dynamics, then plug the data into a music recommendation engine — software designed to find songs that share similar traits.

“It’s about understanding someone’s music taste,” said Savage Beast founder Tim Westergren. “Why does somebody like a piece of music?”

Employees go through weeks of training before they can recognize such song attributes as the degree of vibrato in a singer’s voice, a trait that could represent the difference between a song by     Karen Carpenter or Mariah Carey.

Westergren, who previously played in bands and worked as a composer, compares the concept to isolating the spices, sauces and other ingredients that might go into a given meal and determining which will appeal most to the consumer.

While he doesn’t believe his company can fully decipher everyone’s musical taste, Westergren argues that his system comes closer than models that recommend songs based only on what other music fans have bought.

Savage Beast’s customers include AOL Music, Best Buy Co. and Borders Group Inc.

AOL says its music section traffic has increased by 20 percent since it began using the program in February. BestBuy, uses it at listening stations in 14 stores; Borders uses it at 12 stores, Westergren said.

A similar company, San Francisco-based Siren Systems, also takes songs apart before they’re placed in a preference database.

Siren Systems users access its recommendation engine through the Soundflavor.com Web site. Users can search its Web site by artist, song or album to receive suggestions for similar music. They can also search other computer users’ playlists.

Run from a basement office that’s adorned with CDs dangling from sprinkler heads, Siren also licenses its engine to businesses such as MediaSpan Group, which builds Web sites for radio stations and other firms.

Like Savage Beast, Siren trained and tested its roughly 20 song taggers, who include part-time musicians, for several weeks to recognize its array of song attributes like “horizontal density” — a measure of how many musical elements are packed into a song.

“The point of our recommendation technology is not necessarily to identify the new hit, it is to help you to be a good editor” of song collections, said Siren chief executive Steve Skrzyniarz.

For some music retailers, such recommendation models aren’t all that attractive.

“Some of the technology-only models are wrong,” said Laura Goldberg, chief operating officer of     Napster. “They don’t necessarily follow user habits.”

Napster uses a hybrid system operated by Cambridge, Mass.-based MediaUnbound that employs data from subscriber playlists, genres and file-sharing networks. Music editors can also recommend songs or artists.

“I’m skeptical that it would be any better and probably worse than looking at people who bought this album also bought this album,” said digital music analyst Phil Leigh.

The recommendation engines’ choices can seem hit or miss.

Clothing merchandiser Jaime Parilla, 36, found most of the Savage Beast-picked selections at a listening station at BestBuy in West Hollywood were on the mark, but others were just strange.

The suggestions led him from vocalist Amerie to Vanessa Williams and Latin singer Thalia. Williams’ music seemed to have the same mood as Amerie, but he was puzzled at the suggestion of Thalia.

A search for U2 returned 1980s balladeer     Richard Marx,     David Bowie and     Eddie Money, among others. The Marx suggestion might not seem like much of a match beyond the fact that U2’s Bono and Marx both sported mullets in the 1980s.

Another search, looking for tracks similar to Madonna’s 1980s pop hit “Like a Virgin,” turned up “Talking Back to the Night” by singer     Joe Cocker.

The midtempo pop number, which is laden with electronic keyboard and other production touches, is not typical of the crooner’s better known soulful fare.

But it does share many elements with the Madonna classic.

“One thing we’re trying to do is not think of an artist in terms of how they might be labeled in terms of the marketing of music, and be true to the recording itself,” Westergren said. “That means we might match up artists that intuitively might not be musical neighbors.”

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Savage Beast Technologies: www.savagebeast.com

Siren Systems: www.soundflavor.com