Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts and how the
Speaker 1: tech are you. You might be able to tell from
Speaker 1: my voice that I have a cold, so I apologize
Speaker 1: for that. But we're going to soldier on because I'm
Speaker 1: back from vacation. It's time to get back to work,
Speaker 1: and I love to talk about the intersection of technology
Speaker 1: and music. So in past episodes, I've done shows about
Speaker 1: how electric guitars work, the history of the Moog or
Speaker 1: Mogue synthesizer, the evolution of various kinds of recordable media,
Speaker 1: and much much more. But way back, like back in
Speaker 1: two thousand and nine, my co host at the time,
Speaker 1: Chris Palette, and I did a little episode of tech
Speaker 1: Stuff about auto tune, and I thought it would be
Speaker 1: fun to go back and revisit that topic. So this
Speaker 1: is not a rerun. It's an all new episode about
Speaker 1: the same subject. I haven't even listened to the old episode,
Speaker 1: so I have no idea how much of what I
Speaker 1: have to say is going to be a repeat. I
Speaker 1: imagine a lot of it will be new, but I
Speaker 1: don't know for sure. I figure there will be fewer
Speaker 1: puns in this version compared to the last one, because,
Speaker 1: contrary to popular belief, it was actually Chris Pillette who
Speaker 1: made the most puns on tech stuff back in the day.
Speaker 1: I got a reputation for it, and I don't get
Speaker 1: me wrong, I won't shy away from a good pun,
Speaker 1: and by that I mean a terrible pun. I love them,
Speaker 1: but Chris like he loved them the way I love,
Speaker 1: you know, rich Indian food, he dined upon puns, So
Speaker 1: probably not as many in this one. But let's talk
Speaker 1: about auto tune now. I think just about everyone knows,
Speaker 1: or I think a lot of people know that Shares
Speaker 1: song Believe, which came out nineteen ninety eight, was the
Speaker 1: first major song to prominently use auto tune in an
Speaker 1: effort to achieve a particular artistic effect, but the technology
Speaker 1: had been around for more than a year at that point,
Speaker 1: and the original intention wasn't to make a tool though
Speaker 1: it actually draw attention to itself. Rather as the name
Speaker 1: autotune suggests, it was intended to automatically nudge the pitch
Speaker 1: of a musical note in the right direction so that
Speaker 1: it would be in tune. That way, the occasional wrong
Speaker 1: note could be subtly pushed into place, and it wouldn't
Speaker 1: require you to do another take and then try to
Speaker 1: splice together a great master recording. But even all of
Speaker 1: that is getting way ahead of ourselves. To understand the
Speaker 1: history of autotune, we must first learn about reflection seismology
Speaker 1: as well as the oil industry. And I am being serious,
Speaker 1: I'm not making a joke about this. As it turns out,
Speaker 1: reflection seismology has a lot to do with our story
Speaker 1: because the man who would go on to found the
Speaker 1: company that would create autotune was a doctor, Andy Hildebrand,
Speaker 1: who had made a career in using sound and complex
Speaker 1: mathematical calculations to help oil companies, namely Exon, locate oil
Speaker 1: deposits underground. So reflection seismology is in some ways similar
Speaker 1: to sonar. So with a sonar system, you would beam
Speaker 1: out pulses of sound waves. Typically we talk about this
Speaker 1: in water, right, Like using sonar on a boat or
Speaker 1: on a submarine, that kind of thing. You would pulse
Speaker 1: out these sound waves and those soundwaves travel outward from
Speaker 1: the source from the speaker. Essentially the transmitter, and if
Speaker 1: there's something solid in the way of those sound waves, well,
Speaker 1: the sound waves that hit that solid object, they're going
Speaker 1: to reflect back toward the source. They'll become an echo.
Speaker 1: This is what we get when we hear an echo.
Speaker 1: If you are ever in a place where you make
Speaker 1: a loud noise, then you hear the echo. It's because
Speaker 1: the sound waves have traveled out from you, bounced off
Speaker 1: something and came back to you. Well, if you if
Speaker 1: you measure the amount of time it took for a
Speaker 1: sound to leave you and then reflect off something else
Speaker 1: and come back to you, you can figure out how
Speaker 1: far away you are from that thing, right, because sound
Speaker 1: is going to travel a specific speed away from you
Speaker 1: and then hit the thing and then travel back to you.
Speaker 1: So if you know how long it took, you can
Speaker 1: do some very simple math and figure out how far
Speaker 1: away that object is. So, for example, if you're on
Speaker 1: a ship and use sonar to measure the distance between
Speaker 1: you and the sea floor, you do a little math. Right,
Speaker 1: you have to divide by two because it took a
Speaker 1: certain amount of time to travel down then back up,
Speaker 1: And you have to know how fast sound travels through water.
Speaker 1: You have to have all these important bits of information
Speaker 1: in your mind when you do this, but then you
Speaker 1: can suss that out. You can say how deep the
Speaker 1: ocean floor is from the surface. That saves you the
Speaker 1: trouble of having to do it the really old fashioned way,
Speaker 1: which typically involved lowering a weight on the end of
Speaker 1: a knotted line, a knotted rope, and then you use
Speaker 1: the knots to keep count of how deep in the
Speaker 1: ocean you were. That's a sounding line. That's the other
Speaker 1: way to see how how far down the ocean floor is.
Speaker 1: But sonar made it way simpler, especially once you were
Speaker 1: able to build that math into the sonar workstations. Reflection
Speaker 1: seismology does something similar, but with seismic waves, and those
Speaker 1: are waves that pass through the earth, and we typically
Speaker 1: talk about seismic waves in connection with earthquakes or like
Speaker 1: volcanic eruptions that kind of thing, and in fact earthquakes
Speaker 1: were what inspired smarting Pants is to say, hey, if
Speaker 1: we made something that could you know, create a huge
Speaker 1: vibration through the earth, and something else that could detect
Speaker 1: those vibrations, and we were able to calculate how long
Speaker 1: it took for the instrumentation to pick up on the
Speaker 1: echoes of that initial vibration event, we might be able
Speaker 1: to figure out stuff that's actually underground. We could figure
Speaker 1: out what is underground without having to dig it up
Speaker 1: and see. Now, that's because the seismic waves will travel
Speaker 1: at different speeds depending upon the density of the material
Speaker 1: that they travel through. You've probably heard things like, you know,
Speaker 1: sound travels at a consistent speed. That's true, but that
Speaker 1: consistent speed is dependent upon the medium through which the
Speaker 1: sound is traveling. So sound travels at a different speed
Speaker 1: through the water than it does through the air or
Speaker 1: through solid objects. You know, vibrations travel at different speeds
Speaker 1: depending upon the medium. So at a very basic level,
Speaker 1: a seismic wave will travel at a constant rate through
Speaker 1: one kind of say rocky soil. But let's say there's
Speaker 1: a place underground where that rocky soil gives way to
Speaker 1: a different material says petroleum for example. Well, then the
Speaker 1: speed of those sound waves is going to change. Moreover,
Speaker 1: as the sound waves hit that barrier between one type
Speaker 1: of material and another, some of the sound waves are
Speaker 1: going to reflect off of that and become an echo.
Speaker 1: Some of the sound waves will continue to penetrate through
Speaker 1: the new material and through lots of observations. We gradually
Speaker 1: began to learn about the different rates at which a
Speaker 1: seismic wave will travel depending upon the medium it's traveling through,
Speaker 1: and if it hits something really solid like bedrock, it
Speaker 1: pretty much just echoes back. So here's how reflection seismology works.
Speaker 1: From a very high level. You set up sensitive equipment
Speaker 1: at different distances from a blast sight, and yeah, you're
Speaker 1: likely to use something like explosives or maybe a really
Speaker 1: powerful air gun. It has to be something that's going
Speaker 1: to give a real jolt to the ground in order
Speaker 1: to do this, because that's essentially what you're doing is
Speaker 1: creating like a very localized earthquake. So this vibration travels
Speaker 1: through the earth, and because you know how far away
Speaker 1: you've set up your measuring equipment from that blast site,
Speaker 1: you already have distance figured out, right. You know how
Speaker 1: far away it is from the original source of the vibration,
Speaker 1: and you measure the time it takes for your equipment
Speaker 1: to pick up the echoes from that particular vibration event.
Speaker 1: So you've got distance and now you have time. Now
Speaker 1: you've got those variables sorted, so you can start to
Speaker 1: work out what material is actually under the ground that
Speaker 1: produces this particular result. And by doing that, you're kind
Speaker 1: of like working backwards. You're using this information to draw
Speaker 1: conclusions about what's under there, and that's where you can
Speaker 1: start to make a determination as to whether or not
Speaker 1: you're standing on top of a Beverly hillbilly is like
Speaker 1: oil deposit, or maybe you're just on top of a
Speaker 1: bunch of rocks or whatever. Now, in order to do
Speaker 1: that what I just described, it's actually incredibly complicated. It
Speaker 1: involves an awful lot of calculations in math, and it's
Speaker 1: a lot of work. But then you have to think
Speaker 1: that drilling for oil is even more work. That's a
Speaker 1: huge endeavor. It costs a lot of time and money
Speaker 1: and effort to do it, and like if you drill
Speaker 1: in the wrong place, like that's a huge loss. So
Speaker 1: you want the best possible information before you select a
Speaker 1: drilling site, and reflection seismology is one way to obtain
Speaker 1: information and to help make a decision. So doctor Hildebrand
Speaker 1: was making a really good living out of this work,
Speaker 1: but companies like Exon were saving hundreds of millions of
Speaker 1: dollars through Hildebrand's approach of narrowing down potential drill sites,
Speaker 1: and Hildebrand thought, you know, I'm not doing badly. I'm
Speaker 1: making a decent living. But you know, Exon is making
Speaker 1: out like a bandit. They're saving like half a billion
Speaker 1: dollars a year or whatever using this technology. Maybe if
Speaker 1: I apply my knowledge and skill set in a company
Speaker 1: that I own, I might actually, you know, do better
Speaker 1: than just working for Exon. So Hildebrand left Exon in
Speaker 1: nineteen seventy nine and he founded a company called Landmark Graphics,
Speaker 1: which at first sounds like, you know, it's a company
Speaker 1: that makes computer graphics, which is not untrue, but that
Speaker 1: wasn't It wasn't just general graphics. This company was still
Speaker 1: rooted in the oil industry. Hildebrand's team developed and produced
Speaker 1: workstations that could take incoming seismic information from these these
Speaker 1: you know, soundings that they do and generate three dimensional
Speaker 1: seismic maps based upon the data. And again, it was
Speaker 1: incredibly complicated. You had to analyze so many different points
Speaker 1: of information in order to create this three dimensional representation
Speaker 1: of what's under the ground. But it worked and it
Speaker 1: made Hildebrand very successful. He stuck with it for a
Speaker 1: decade until nineteen eighty nine, whereupon he retired and he
Speaker 1: decided to return his attention to a different passion he
Speaker 1: had had since he was a kid, which was music.
Speaker 1: Now Hildebrand wasn't just a music fan, he was a musician.
Speaker 1: He had played flute professionally. He had been a studio
Speaker 1: musician for some time. He had paid his way through
Speaker 1: college partly by giving flute lessons to musicians, So he
Speaker 1: decided he would go back to school as a retiree
Speaker 1: and study composition and techniques. He attended Rice University to
Speaker 1: do this. While he was back in college, he encountered
Speaker 1: some newer technologies in the music space, like music samplers
Speaker 1: and synthesizers. So these were machines designed to take a
Speaker 1: sample of a sound like a flute, and then allow
Speaker 1: a keyboard musician to recreate those sounds on a synthesizer.
Speaker 1: The only thing is that Hildebrand thought they sounded terrible,
Speaker 1: and partly it was because there was a limitation on
Speaker 1: how much data a synthesizer could actually handle, so it
Speaker 1: couldn't really replicate sound naturally. The sound it replicated would
Speaker 1: be like a gross approximation of the original sound, So
Speaker 1: Hildebrand wasn't really impressed, but he thought that there was
Speaker 1: room for improvement, and he developed a technique to compress
Speaker 1: audio data so that synthesizers could more effectively handle information
Speaker 1: and make notes, to produce notes that sounded more natural
Speaker 1: and less synthetic. He released his software as a product
Speaker 1: called Infinity, and while this tool would revolutionize the orchestration
Speaker 1: process for stuff like film and television, it did not
Speaker 1: revolutionize doctor Hildebrand's bank account. He didn't actually see much
Speaker 1: of that success himself because what actually happened was other
Speaker 1: companies purchased copies of Infinity and then bundled it with
Speaker 1: their own audio processing tools, and then sold those audio
Speaker 1: processing packages to other people and companies, and it kind
Speaker 1: of cut Hildebrand out of the picture. So while others
Speaker 1: were benefiting from his work, he did not see that
Speaker 1: much success. It did, however, again have an enormous impact
Speaker 1: on orchestrations, like According to doctor Hildebrand, he was the
Speaker 1: reason why the Los Angeles Orchestra hit real hard times
Speaker 1: in the nineteen nineties because his tools allowed composers to
Speaker 1: sample various musical instruments and create a natural enough representation
Speaker 1: of those sounds to be able to create a synthetic
Speaker 1: orchestra that sounded more or less like a real one.
Speaker 1: So there was no need to go and hire a
Speaker 1: real orchestra to orchestrate your film or TV project. You
Speaker 1: could do it yourself. I've actually heard some some of
Speaker 1: my favorite music scores. When I listened closely, I can
Speaker 1: tell like, oh, that's not a real cellist. That's a
Speaker 1: synthesizer playing a sample of a cello that sounds almost,
Speaker 1: but not quite like the real thing. Anyway, we can
Speaker 1: thank doctor Hildebrand for that. I'll talk more about what
Speaker 1: we could thank doctor Hildebrand for, specifically auto tune, but
Speaker 1: first let's take a quick break so we could thank
Speaker 1: some other people, namely our sponsors. Will be right back. Okay,
Speaker 1: So before we left off, I was talking about how
Speaker 1: doctor Hildebrand had released a program called Infinity that improved
Speaker 1: the performance of synthesizers and samplers. But in nineteen ninety
Speaker 1: he decided to take an extra step. He founded a
Speaker 1: new company. He called it Antare's Audio Technology, and this
Speaker 1: would be his music company, his music technology company that
Speaker 1: would ultimately produce autotune. And he knew that technology was
Speaker 1: poised to make a huge impact on the music industry
Speaker 1: and already had been like, that's kind of the history
Speaker 1: of modern music is how technology has shaped it. But
Speaker 1: he knew we were on the brink of another revolution.
Speaker 1: He just wasn't exactly sure how that was going to
Speaker 1: manifest now. According to an article by Simon Reynolds, it's
Speaker 1: titled How Autotune Revolutionized the Sound of Popular Music, and
Speaker 1: it was published in Pitchfork, the actual birth of Hildebrand's
Speaker 1: idea for autotune grew out of a casual lunch with
Speaker 1: some of his friends and peers back in nineteen ninety
Speaker 1: five during a National Association of Music Merchants conference. So
Speaker 1: he's at this conference, he's meeting with other people in
Speaker 1: the music and technology spheres, and at this lunch, one
Speaker 1: of the attendees jokingly suggested that what Hildebrand should do
Speaker 1: next is develop a technology that would allow her to
Speaker 1: sing on key, like, can you make a box that
Speaker 1: lets me sing well? And while this was presented as
Speaker 1: a joke, ultimately Hildebrand would think, huh, could I do
Speaker 1: that now? According to Zachary Crockett's article, which is the
Speaker 1: Mathematical Genius of auto Tune, this one in price Anomics
Speaker 1: This wasn't like a light bulb moment where the moment
Speaker 1: this woman says the thing, Hildebrand immediately thinks, ah, that's
Speaker 1: what I shall do. Actually, it took like another six
Speaker 1: months before Hildebrand really kind of revisited the concept and thought,
Speaker 1: maybe there's something here. But in order to do that,
Speaker 1: he would have to develop a technology that could do
Speaker 1: a few things really well, all of which are a
Speaker 1: bit tricky. One is it would need to detect the
Speaker 1: pitch that someone was singing in. For example, if you're
Speaker 1: using it for vocals, and so you would need to
Speaker 1: be able to detect exactly the frequency that was being sung.
Speaker 1: You would need to then also be able to have
Speaker 1: a list of tones that were in the whatever key
Speaker 1: you were supposed to be singing it. So I don't
Speaker 1: want to get into music theory, because goodness knows, I
Speaker 1: don't know that much about it myself, and I would
Speaker 1: just mess things up. But you know, if you're singing
Speaker 1: in a specific key, there are particular tones that belong
Speaker 1: to that key. And often when we sing and we're
Speaker 1: a little off pitch, what we need is to be
Speaker 1: gently nudged a little up or a little down, a
Speaker 1: little sharp or a little flat in order to hit
Speaker 1: a semitone that belongs in that key. So it needs
Speaker 1: to also quote unquote know which tones are appropriate, and
Speaker 1: then it has to be able to digitally alter the
Speaker 1: incoming pitch the actual sung note, and then guide it
Speaker 1: to match that of a target note. Now, ultimately that
Speaker 1: all sounds like a pretty simple idea, but in reality
Speaker 1: to achieve this it was incredibly complex. Ultimately, also, the
Speaker 1: toolould need to work in real time for live performances.
Speaker 1: Like it's one thing to have this for the studio, right,
Speaker 1: because even if you don't have an automatic, you could
Speaker 1: have a tool where an engineer could fiddle with some
Speaker 1: controls and gently alter the pitch of a performance to
Speaker 1: get it closer to being where it needs to be.
Speaker 1: It would be preferable to have that automated so that
Speaker 1: you don't have to go through there and do the
Speaker 1: manual process. But even so, like in a recording setting,
Speaker 1: you don't have to have it be real time necessarily,
Speaker 1: but if you're doing a live performance, you do have
Speaker 1: to have a real time. If someone's up there singing
Speaker 1: and they just hit a flat note when they're not
Speaker 1: supposed to, that could really be a memorable moment and
Speaker 1: not in a great way. So having a tool that
Speaker 1: could gently account for that and fix it in real
Speaker 1: time would be really helpful. But this would mean that
Speaker 1: this tool would have to be able to process a
Speaker 1: huge amount of sound data extremely quickly to make millisecond
Speaker 1: decisions like split millisecond decisions relating to how to shape
Speaker 1: a note moment by moment. Now it does help if
Speaker 1: we also think of sound in terms of mathematics. We
Speaker 1: describe sound in different ways, right, But some of those
Speaker 1: relate specifically to how sound looks to us. If we
Speaker 1: plot sound on like a wave chart, right. For example,
Speaker 1: sounds can be really loud or they can be really quiet,
Speaker 1: and that is volume, But it can also relate to amplitude.
Speaker 1: When you think of a sound wave. The amplitude of
Speaker 1: a sound wave describes how tall those peaks are or
Speaker 1: how low the valleys are. The distance between the furthest
Speaker 1: point of a peak or valley and the zero line.
Speaker 1: That's your amplitude. But we also describe sound in terms
Speaker 1: of pitch or frequencies. Higher frequencies correspond to higher pitches,
Speaker 1: And if we plot a sound wave, let's say that
Speaker 1: we plot it so that the x axis is a
Speaker 1: demarcation of time, so we have one second listed there,
Speaker 1: like the x axis is one second. If there's one
Speaker 1: wave that we draw so that the wave begins at
Speaker 1: the zero point and ends at the one second point,
Speaker 1: then we have a one hurtz sound wave. A hurtz
Speaker 1: is just a measurement a frequency. It refers to one
Speaker 1: cycle per second. So if a wave is one hurts,
Speaker 1: it means it takes one second for one of those
Speaker 1: sound waves to fully pass a given point where you're
Speaker 1: measuring the sound waves, right, If two waves pass that
Speaker 1: point within one second, then you're talking about two hurts,
Speaker 1: you know. Just so that we know, the typical human
Speaker 1: hearing range is anywhere between twenty and twenty thousand hurts.
Speaker 1: So one or two hurts sound We wouldn't even perceive it,
Speaker 1: at least not as sound. If it was a great
Speaker 1: enough amplitude, you could potentially perceive it as vibration, but
Speaker 1: you wouldn't feel it, you wouldn't hear it. But between
Speaker 1: twenty and twenty thousand hurts, that falls into the typical
Speaker 1: range of human hearing. Of course, as we get older,
Speaker 1: we start to lose the ability to hear those higher frequencies.
Speaker 1: These days, I think my hearing tops out around sixteen
Speaker 1: to seventeen thousand hurts somewhere around there. Like once you
Speaker 1: get beyond that, I don't hear anything, whereas younger people
Speaker 1: could hear it. Anyway, Hildebrand was working with music on
Speaker 1: this mathematical level. He was analyzing music to recognize where
Speaker 1: the frequencies were and where they should be, and to
Speaker 1: then shape the sound wave so that it would fit
Speaker 1: what the ideal would be where it would be on key.
Speaker 1: He was not the first person to attempt to do this, however,
Speaker 1: Earlier engineers had largely abandoned the quest because the signal
Speaker 1: processing and statistical analysis needs were so high. They were
Speaker 1: so extreme that you would need a supercomputer dedicated to
Speaker 1: the task to be able to do it. There's just
Speaker 1: too much data to process in too little time to
Speaker 1: be able to do anything meaningful with it. Hildebrand determined
Speaker 1: that yeah, to fully analyze music, you would have to
Speaker 1: run thousands or millions of calculations, but many of those
Speaker 1: calculations were actually redundant at the end of the day,
Speaker 1: and eliminating the redundancy would not affect the quality of
Speaker 1: the outcome, and so in his words he quote changed
Speaker 1: a million multiply ads into just four. It was a trick,
Speaker 1: a mathematical trick. End quote. That's ron the article I
Speaker 1: mentioned earlier by Zachary Crockett. So yeah, in prisonomics, pretty
Speaker 1: phenomenal that he was able to recognize that ultimately he
Speaker 1: just needed these four processes to really be able to
Speaker 1: zero in on pitch correction. So Hildebrand developed the autotune
Speaker 1: technology in nineteen ninety six. He actually used to customized
Speaker 1: Mac computer or specialized Mac computer as the way I've
Speaker 1: seen it explained. I don't know in what way it
Speaker 1: was specialized. I just know it was a Mac. And
Speaker 1: he brought his software to the next National Association of
Speaker 1: Music Merchant's conference, if you remember, that was the same
Speaker 1: conference where one of his lunch companions had inspired the
Speaker 1: idea for autotune in the first place. To say that
Speaker 1: he felt interest in his product at this conference is
Speaker 1: really under selling it, and it's understandable why. So let's
Speaker 1: talk about the process of creating a master recording for
Speaker 1: a song. If you want to get a perfect take
Speaker 1: of a song, where this is the master recording, this
Speaker 1: is what you want to use in order to you know,
Speaker 1: create your album. You can't just hope that everything lines
Speaker 1: up when you hit record and that everyone is playing
Speaker 1: seamlessly together and no one makes a mistake. Invariably something
Speaker 1: is going to be off. Maybe one of the musicians
Speaker 1: is lagging behind the others and it might not even
Speaker 1: be detectable at first, but upon closer examination you're like, ooh,
Speaker 1: you came in late, or you came in too early
Speaker 1: or whatever. Or the drummer is not keeping perfect time,
Speaker 1: whatever it may be. Maybe someone hits a wrong note,
Speaker 1: either while playing an instrument or while singing, or maybe both.
Speaker 1: But what it means for engineers is that they'll need
Speaker 1: to get another take where that mistake isn't there, and
Speaker 1: they'll probably need another take and another take, And if
Speaker 1: you want the perfect performance, this could mean recording the
Speaker 1: same track dozens or gosh even hundreds of times and
Speaker 1: then slowly picking apart each recording in order to piece
Speaker 1: together a perfect edit. And that alone is hard because
Speaker 1: just lining up the different takes isn't always the easiest
Speaker 1: thing to do. You don't always have a seamless point
Speaker 1: where you could line up take one would take two.
Speaker 1: Like again, if the band is playing at a slightly
Speaker 1: different pace in the second take. You can't easily line
Speaker 1: up the two different ones to you know, even if
Speaker 1: like one had an accident and the other one didn't,
Speaker 1: you can't necessarily put them together to make the perfect recording.
Speaker 1: So this is a really laborious, time consuming and expensive process.
Speaker 1: Expensive because studio time is limited, so expensive. Hildebrand's invention
Speaker 1: would take a ton of that effort off the table,
Speaker 1: at least for vocals, because rather than re recording a
Speaker 1: billion times, you could get maybe just one good take,
Speaker 1: one decent take even and then use pitch correction for
Speaker 1: any little flubs that might have found their way in
Speaker 1: during the recording process. So it was a huge time saver,
Speaker 1: and time is money. So immediately studios recognized the value
Speaker 1: of Hildebrand's product and they rushed to get in on that,
Speaker 1: and the tool absolutely revolutionized the recording industry. Studios that
Speaker 1: incorporated auto tune were able to work much faster than
Speaker 1: their competitors. They were able to cycle clients in and
Speaker 1: out of their studios more quickly. That meant getting more
Speaker 1: work done and more money coming in, and efficiency skyrocketed.
Speaker 1: So studios that were not on the auto tuned train
Speaker 1: soon found themselves getting out competed, and they ended up
Speaker 1: adopting the technology as well, because it was either adopted
Speaker 1: or go out of business. It also wasn't enough to
Speaker 1: just be able to change the pitch of a note.
Speaker 1: Auto tune would also have to be able to adjust
Speaker 1: that pitch on a sliding scale of rapidity. That is,
Speaker 1: the sound would be unnatural if you were to correct
Speaker 1: a note instantaneously. It would be the effect that we
Speaker 1: associate with autotune, that robotic effect. That's if you were
Speaker 1: to change the pitch correction super fast. You don't want
Speaker 1: to do that if you want the tool to remain unnoticed. So,
Speaker 1: particularly for stuff like slow ballads, you would not have
Speaker 1: a more gradual approach to correcting a pitch. So Hildebrand
Speaker 1: wanted a tool that would let users determine how quickly
Speaker 1: the note would get nudged to the correct pitch, and
Speaker 1: the scale essentially went from zero to ten. The higher
Speaker 1: settings would have longer adjustment times, so for a really
Speaker 1: slow song, you might go with a nine or a
Speaker 1: ten to let the note find its way to the
Speaker 1: right pitch more gradually. Faster songs like rock and roll
Speaker 1: type stuff or a rap or R and B. You
Speaker 1: might require a lower setting, like fast rock songs, you
Speaker 1: might need a two, three, or maybe even down to
Speaker 1: a one. The zero setting. Really, Hildebrand just added that
Speaker 1: for kicks, So essentially the software would immediately correct the
Speaker 1: pitch upon detecting an incoming signal. And this sounded weird
Speaker 1: and unnatural, and it was obvious that something was going on.
Speaker 1: So this was more for fun than an intent to
Speaker 1: create a new tool for musicians. But it turned out
Speaker 1: that's exactly what autotune was really destined for, to become
Speaker 1: a tool for a process called pitch quantization. But again,
Speaker 1: that wasn't what Hildebrand set out to do. In fact,
Speaker 1: ecquate to that Pitchfork article I mentioned earlier, the idea
Speaker 1: here was to aim for perfection, at least in terms
Speaker 1: of being in the right key and on pitch. That
Speaker 1: imperfections would somehow interfere within a momotional connection to the music,
Speaker 1: and you want that music to be perfect so that
Speaker 1: you can have that emotional impact. Now, personally, I disagree
Speaker 1: with that take. Some of my favorite recordings are with
Speaker 1: artists who have imperfect voices. They weren't screeching or catterwalling.
Speaker 1: It wasn't like it was unpleasant to listen to them,
Speaker 1: but they aren't pitch perfect either, and to me, that
Speaker 1: adds a lot of character and emotion. So as an example,
Speaker 1: Warren Zevon, who you know did the song where Wolves
Speaker 1: of London and tons of other stuff. I mean, prolific
Speaker 1: musician who tragically passed away several years ago. He has
Speaker 1: a great cover of the song back in the High
Speaker 1: Life Again and which is a pretty cheesy song, but
Speaker 1: Warren Zevon's cover is really emotional. It's great, and it's
Speaker 1: a little bit raw, and to me it resonates far
Speaker 1: more than a note perfect performance would have. But I
Speaker 1: do understand where hill le Brand and his team were
Speaker 1: coming from. You know, if you if you have a
Speaker 1: take from a recording session that is almost but not
Speaker 1: quite right, maybe there was a transition where the wrong
Speaker 1: note came out, or you know, just a moment where
Speaker 1: it took an artist a little bit longer to slide
Speaker 1: to find the right pitch. A tool that could smooth
Speaker 1: things out a little while not remaining you know, noticeable,
Speaker 1: while you know, slipping under the radar. That could prevent
Speaker 1: listeners from being distracted by something that was unintentional. But
Speaker 1: what if you took that tool that was meant to
Speaker 1: fix errors and used it to create unintended effects. That's
Speaker 1: what we're going to talk about when we come back
Speaker 1: from this quick break. So we talked about how auto
Speaker 1: tune was meant to fix little imperfections in music recordings
Speaker 1: and live performance. But as I mentioned, if you had
Speaker 1: that setting set to zero so that it would instantaneously
Speaker 1: attempt to correct pitches, then you could create an almost
Speaker 1: robotic vocalization. So instead of shying away from the artificial
Speaker 1: sounds that could come out if you were to use
Speaker 1: it improperly, you leaned into it. That's what happened in
Speaker 1: nineteen ninety eight with Sher's song Believe, saying that dolls
Speaker 1: zero would create the robotic like effect, which in this
Speaker 1: case was the goal in the first place, and that
Speaker 1: song was a smash success. I couldn't stand it, and
Speaker 1: it was everywhere. I couldn't stand it, not because of
Speaker 1: the auto tune. I just didn't vibe with the song.
Speaker 1: No no shade on chare phenomenal artist, you know, incredibly talented,
Speaker 1: Just that song didn't jibe with me and The interesting
Speaker 1: thing was that this huge success not only pulled the
Speaker 1: curtain back on a tool that was meant to correct
Speaker 1: little mistakes and thus create a whole conversation around whether
Speaker 1: or not artists were quote unquote cheating by using it,
Speaker 1: but it launched a whole new way to create music
Speaker 1: in the first place. I personally think the artist who
Speaker 1: is most associated with auto twoun is one who adopted
Speaker 1: the technology and made it an intrinsic part of his brand.
Speaker 1: That would be te Pain. He came to the party
Speaker 1: a little bit late. He became interested in autotune around
Speaker 1: two thousand and four, and he wasn't looking for something
Speaker 1: to help compensate for his singing ability, because he actually
Speaker 1: sings very well. But he liked the thought of the
Speaker 1: technology that would set him apart from other artists, and
Speaker 1: he could forge a vocal identity using this tool to
Speaker 1: create a sound that no one else was really embracing
Speaker 1: at that point. So he jumped wholeheartedly into autotune, and
Speaker 1: he made liberal use of the technology and achieved tremendous
Speaker 1: success along the way, selling like Platinum records by using
Speaker 1: this technology. His love of the software led to an
Speaker 1: official partnership with Hildebrand's company for a few years, and
Speaker 1: Tarees licensed the technology to tee Pain for an app
Speaker 1: called I Am te Pain, which you could use to
Speaker 1: do autotuned right there on your smartphone three dollars initially,
Speaker 1: and it was downloaded by millions of users that generated
Speaker 1: quite a lot of revenue just on its own. Now,
Speaker 1: eventually t Pain and Antarees parted ways, and t Pain
Speaker 1: ultimately partnered with a different pitch correction company called Isotope.
Speaker 1: The t Pain story also led to a lawsuit against Antarees,
Speaker 1: and Antaries filed a countersuit against te Pain, and ultimately
Speaker 1: the whole thing was settled out of court and everyone
Speaker 1: signed an NDA. So I have no details about, you know,
Speaker 1: how that shook out in the end, but it was
Speaker 1: one of those things where it was kind of a
Speaker 1: smudge on the Antarees name at least it was awkward, right.
Speaker 1: But a much larger threat to Hildebrand's company was Apple.
Speaker 1: Apple had purchased a German company called Emagic. Emagic also
Speaker 1: had a pitch correction tool. In fact, it was a
Speaker 1: pitch correction tool that, according to Hildebrand, essentially copied autotune technology.
Speaker 1: This was possible because Antaris had failed to protect its
Speaker 1: German patent properly, and so Emagic was able to appropriate
Speaker 1: that technology or copy that technology without fear of legal recourse.
Speaker 1: So then Apple acquires Emagic, which means Apple is then
Speaker 1: able to incorporate Emagic's technology into their own products, including
Speaker 1: their own sound editing software. And this meant that autotune
Speaker 1: effectively got incorporated into Apple software without having to license
Speaker 1: the technology from Antaries, because again they got it by
Speaker 1: acquiring this German company. Now, Antaris could technically have still
Speaker 1: sued Apple. There's no guarantee that they would have won,
Speaker 1: but they could have sued them. However, Hildebrand explained that
Speaker 1: they didn't really have that option because Apple has enorm
Speaker 1: mislead deep pockets. Apple is just an incredibly rich company,
Speaker 1: and Apple could easily just outweight Antaries in the legal system,
Speaker 1: while Antaries would drain its resources trying to sue Apple.
Speaker 1: So even if Antaris was in the right of it,
Speaker 1: even if they would have won a judgment against Apple,
Speaker 1: the chance was that Antari's would go out of business
Speaker 1: just trying to pay for all the legal fees for
Speaker 1: the whole battle in the first place. So Ultimately, Antari's
Speaker 1: didn't go after Apple. It just it would have been
Speaker 1: a death sentence. Culturally, autotune began to face resistance in
Speaker 1: the late two thousands. Some artists expressed disdain for the technology,
Speaker 1: going so far as to say it ruined Western music.
Speaker 1: This was partly due to an oversaturation problem. The success
Speaker 1: of Tea Pain, as well as the earlier instances of autotune,
Speaker 1: inspired countless others to embrace the technology while not necessarily
Speaker 1: doing very much else to differentiate themselves from other artists.
Speaker 1: In other words, they were kind of leaning on it
Speaker 1: as a crutch or a gimmick. So there was a
Speaker 1: glut of auto tune robotic voiced vocals and music in
Speaker 1: the early to mid two thousands, and by the late
Speaker 1: two thousands some folks were absolutely fed up with this
Speaker 1: and there was a backlash. It actually kind of reminds
Speaker 1: me about how people began to turn against disco in
Speaker 1: the nineteen seventies, and that in some ways the punk
Speaker 1: rock movement was partly a reaction to disco or a
Speaker 1: rejection of disco. I would only say partly because punk
Speaker 1: rock also has its roots in glam rock, and I
Speaker 1: think glam rock also kind of helped inspire disco. So
Speaker 1: it's a complicated set of relationships, as you might say
Speaker 1: on Facebook. But bands like Death Kem for Cuti actually
Speaker 1: actively spoke out against autotune. So again, some artists were
Speaker 1: arguing that autotune was being used by people to compensate
Speaker 1: for a lack of ability, So they're kind of casting
Speaker 1: shade on fellow artists saying, well, yeah, they have to
Speaker 1: use autotune because they can't sing, or others would say
Speaker 1: like it was making music less genuine and sincere, like
Speaker 1: less human because it was going through this digital processing process.
Speaker 1: Jay Z famously released a song titled Death of Autotune
Speaker 1: in two thousand and nine, the same year when our
Speaker 1: original Tech Stuff episode about autotune came out. As you
Speaker 1: might imagine, jay Z's song had some pretty strong opinions
Speaker 1: about the technology inside of it. It resonated enough to
Speaker 1: win him a Grammy for it, so other people agreed.
Speaker 1: But despite all that backlash, autotune continues to be a thing.
Speaker 1: It did not, in fact die. It's been incorporated into
Speaker 1: software and digital audio workstations. It and similar pitch manipulation
Speaker 1: technologies are often found in everything from professional audio engineering
Speaker 1: software suites to free programs that you can download online. So,
Speaker 1: for example, I sometimes use a program called Audacity, and
Speaker 1: Audacity has an option under its effects where I can
Speaker 1: manually adjust the pitch of a recorded piece of audio.
Speaker 1: I can set what the pitch should be. Now that's
Speaker 1: not autotune, right, because by definition I'm not using an
Speaker 1: auto feature. I'm manually changing the pitch, but it's using
Speaker 1: similar approaches to get an effect. I've actually even made
Speaker 1: use of that tool while I was editing my friend
Speaker 1: Shay's podcast, Kadi Womple with the Shadow People. Shae does
Speaker 1: nearly all the voices on that show. I've actually voiced
Speaker 1: two characters on that show. So if you're eager to
Speaker 1: hear other output from me, that's not a technology podcast,
Speaker 1: go listen to Kadi Womple with the Shadow People. I
Speaker 1: voice a couple of characters on that, but I edit
Speaker 1: the show, so I use pitch adjustment tools in order
Speaker 1: to make some of Shay's voices sound like different people.
Speaker 1: So it's still herb doing the voice, but I digitally
Speaker 1: manipulate the voice to give certain characters their own distinct sound.
Speaker 1: It's pretty neat stuff. I have no idea what it
Speaker 1: would sound like if I actually used an auto tuned tool.
Speaker 1: That probably would sound very different, But I have a
Speaker 1: lot of fun playing with these pitch manipulation tools. Now,
Speaker 1: to get more into the cultural and social impact of autotune,
Speaker 1: I highly recommend that article in Pitchfork by Simon Reynolds. Again,
Speaker 1: that's titled how Autotune Revolutionized the sound of Popular Music.
Speaker 1: It's a long form article, it's well worth your time
Speaker 1: to read it. As a Zachary Crockett's article that I
Speaker 1: mentioned earlier, both of those are great articles about autotune
Speaker 1: and not just the technology, but it's impact on music
Speaker 1: in general and society and culture as well. And Reynolds
Speaker 1: goes into much deeper detail about how the technology has
Speaker 1: had an impact on the recording industry and the backlash
Speaker 1: that came out as a result of that, as well
Speaker 1: as sort of a counter movement against autotune. So check
Speaker 1: those out. They are well worth your time. And I
Speaker 1: could go on, but really I feel like those articles
Speaker 1: do a much better job than I would of describing
Speaker 1: all of that, So check those out when you have
Speaker 1: some time. That's it for today. I hope all of
Speaker 1: you out there are doing well, and I will talk
Speaker 1: to you again really soon. Tech Stuff is an iHeartRadio production.
Speaker 1: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows.