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Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.

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Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,

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Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio

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Speaker 1: and I love all things tech and recently I received

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Speaker 1: a tweet from Twitter user Salvatore del Knock, a k

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Speaker 1: A non juror, asking if I would do a breakdown

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Speaker 1: on how analog to digital and digital to analog audio

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Speaker 1: converters work. And that's a great request. Um, it is

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Speaker 1: incredibly technical when you really get down to it. So

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Speaker 1: I'm going to do a very high level view of

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Speaker 1: the concept because otherwise we're gonna have to get into

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Speaker 1: the various methodologies that DAC and a d c's work,

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Speaker 1: and uh, it would quickly become like a technical manual.

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Speaker 1: But if people want that, then I can do a

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Speaker 1: subsequent episode and go into more detail. But one of

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Speaker 1: the things about this is that lets us talk about

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Speaker 1: the differences between analog and digital audio and why converters

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Speaker 1: are necessary in the first place, and to open up

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Speaker 1: the eternal argument about whether one is inherently better than

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Speaker 1: the other. This one goes out to all you audio

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Speaker 1: files out there, so get ready to send me angry messages,

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Speaker 1: because no matter what I say, some of y'all are

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Speaker 1: going to get upset. Anyway, let's start with what it

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Speaker 1: means to be analog versus digital. Now, when I was

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Speaker 1: a young boy, nobody loved me. I was a poor

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Speaker 1: boy from a poor family. No, hang on, that's now,

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Speaker 1: that's Queen's bohemian rhapsty Now, when I was a young boy,

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Speaker 1: analog was the standard. Digital did not even enter into

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Speaker 1: my awareness until I was a teenager, when compact discs

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Speaker 1: were starting to become popular. They had been around for

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Speaker 1: a while before I was a teenager, but I was

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Speaker 1: not really aware of them, because I mean, I grew

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Speaker 1: up in rural Georgia. We would get technology a few

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Speaker 1: years behind everybody else. Anyway, I grew up thinking analog

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Speaker 1: essentially meant old and digital meant new, Like that was

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Speaker 1: the sort of the abstract distinction between the two in

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Speaker 1: my head. But the differences are obviously more complicated than that,

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Speaker 1: and we need to understand how sound works, which I

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Speaker 1: know I've covered many many times, but it's important so

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Speaker 1: that we know how the analog and digital methods of

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Speaker 1: recording and thus reproducing and eventually playing back sound. You

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Speaker 1: know how they work with relation to the original sound

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Speaker 1: that existed. So sound is, when you really get down

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Speaker 1: to it, vibration or pressure waves. Now, we mostly experienced

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Speaker 1: sound by hearing these vibrations travel through the air, but

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Speaker 1: you can also experience this underwater. Sound can move through

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Speaker 1: different media, including solid material. Like if you put your

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Speaker 1: ear against a table, a really long table, and some

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Speaker 1: one on the other end is tapping very lightly on

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Speaker 1: that table, you'll hear it. And it's not because the

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Speaker 1: sound is traveling effectively through the air, though it is

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Speaker 1: doing a little bit of that too, but that it

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Speaker 1: travels through the table to you. Sound also travels at

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Speaker 1: different speeds through different media, and in fact, stuff like

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Speaker 1: air temperature can affect how quickly sound travels, which is

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Speaker 1: why when we talk about the speed of sound, we

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Speaker 1: technically actually need to be a little more specific than that.

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Speaker 1: So the standard way of describing the speed of sound

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Speaker 1: is to say that it moves at three per second

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Speaker 1: in dry air at twenty celsius, that's about sixty eight fahrenheit.

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Speaker 1: And if you start changing those parameters, you know, if

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Speaker 1: you introduce, say a lot of humidity into the air,

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Speaker 1: or you change the air temperature like it goes up

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Speaker 1: or it goes down. Well, sound will travel at a

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Speaker 1: slightly different speed than at that standard I was talking about.

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Speaker 1: Now I could get into how the vibrations cause air

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Speaker 1: molecules to move back and forth, creating little changes in

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Speaker 1: air pressure. And it's these pressure waves, these air fluctuation changes,

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Speaker 1: that our ear drums pick up and transfer to our

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Speaker 1: inner ears. That's where special nerves pick up these fluctuations

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Speaker 1: in our inner ears, and then our brains process those

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Speaker 1: those nerve signals as sound. But most of this isn't

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Speaker 1: important for the rest of this episode, so instead, let's

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Speaker 1: talk about sound waves, all right. So we can think

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Speaker 1: of a vibration as something in which a particle is

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Speaker 1: moved out of its usual place and then it snaps

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Speaker 1: back to its usual place, and it might do this

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Speaker 1: several times. Think of a guitar string. If you pluck

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Speaker 1: a guitar string, you're pulling the string out of where

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Speaker 1: it usually sits, and then it snaps back and forth

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Speaker 1: and oscillates around its normal position until it settles down again.

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Speaker 1: So we can describe the number of times that a

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Speaker 1: particle does as a frequency, you know, or the number

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Speaker 1: of times a string goes from one point all the

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Speaker 1: way across and back to that starting point over the

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Speaker 1: course of a second. So with sound, we usually use

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Speaker 1: the unit hurts to measure frequency. If a particle only

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Speaker 1: did one cycle of vibration per second, if it took

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Speaker 1: a full second for it to go from the you know,

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Speaker 1: the one crest to the next crest, uh, then it

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Speaker 1: would be one hurts. That would also, by the way,

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Speaker 1: be a frequency that was way too low for us

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Speaker 1: to hear. Typical human hearing has a range of around

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Speaker 1: twenty hurts at the low end, to twenty thousand hurts

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Speaker 1: or twenty killer hurts, in other words, on the high end.

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Speaker 1: So for stuff vibrating in a cycle that's twenty times

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Speaker 1: a second all the way up to twenty thousand times

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Speaker 1: a second, that's something we could potentially hear. Now. I

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Speaker 1: say potentially because that is typical human hearing. There are

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Speaker 1: people who can hear outside of that range a little bit,

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Speaker 1: and then there are some of us, especially as we

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Speaker 1: get older, who can hear a more narrow range of frequencies.

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Speaker 1: But frequency is just one part of how we describe sound.

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Speaker 1: We can also describe sound by how loud it is.

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Speaker 1: The volume of sound. So from a physics perspective, we

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Speaker 1: can think of this is how much pressure the sound

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Speaker 1: places upon our ear drums. You know how dramatic those

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Speaker 1: fluctuations and air pressure are. In other words, But loudness

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Speaker 1: isn't just down to physics. The way we experience loudness

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Speaker 1: depends not just on that sound pressure itself, but stuff

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Speaker 1: like psychoacoustics. That's how our brains perceive sound in the

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Speaker 1: first place. But now we've got two criteria we can

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Speaker 1: use to assign to any sound correct Like, we can

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Speaker 1: talk about the frequency of that sound, you know, how

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Speaker 1: frequently that those particles are vibrating, And then we can

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Speaker 1: also talk about the displacement of those particles vibrating, or

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Speaker 1: what we might think of as the loudness or volume

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Speaker 1: of that sound. We could then plot a sound wave

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Speaker 1: as a transverse wave on a graph, and we could

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Speaker 1: have the X axis, you know, the horizontal axis of

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Speaker 1: this graph representing the passage of time. So on the

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Speaker 1: left side we might say zero, and we say time

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Speaker 1: increases as you go to the right. The y axis

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Speaker 1: we could have being displacement, which kind of you know,

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Speaker 1: amplitude or volume in other words, and we could then

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Speaker 1: plot all the points where a particular vibrating particle would

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Speaker 1: occupy over a given span of time. If we had

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Speaker 1: a sound of a steady frequency, then we would end

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Speaker 1: up with a wave that would look a lot like

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Speaker 1: a sign or cosign wave. The distance between two consecutive

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Speaker 1: crests of this wave would be the wavelength for that sound,

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Speaker 1: and the sounds wavelength has an inversely proportional relationship with

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Speaker 1: the sounds frequency, So the higher the frequency of sound,

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Speaker 1: the shorter the wavelength will be. So deep bay Ace

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Speaker 1: notes would have sound waves that have much longer wavelengths

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Speaker 1: than very high pitched high frequency notes. Uh frequency relates

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Speaker 1: to pitch. There isn't like an easy mathematical way we

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Speaker 1: can kind of relate pitch, by the way, There are

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Speaker 1: easy ways we can relate frequencies, but it gets a

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Speaker 1: little tricky anyway. The reason I even talk about plotting

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Speaker 1: sound waves at all is that it makes us easier

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Speaker 1: for us to consider the differences between analog and digital

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Speaker 1: audio recording. Keep in mind, if we plotted that sound wave,

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Speaker 1: that's not that's not the physical sound wave that we've

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Speaker 1: just plotted. That's our description of that sound wave, its frequency,

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Speaker 1: and its loudness. Um The classic sign wave like depiction

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Speaker 1: of the sound wave shows us that there's a continuous

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Speaker 1: representation of sound across time. It is unbroken. We can

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Speaker 1: put plot, you know, even complicated sounds with changes in

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Speaker 1: amplitude and frequency, and the shape of the waves tells

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Speaker 1: us a little bit about the tambre or quality of sound. Now,

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Speaker 1: by quality, I don't mean, oh, this sound is very

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Speaker 1: good quality or this sound is really bad quality. Instead,

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Speaker 1: I'm talking about the elements that differentiate say piano playing

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Speaker 1: middle C from a guitar playing that same note middle C.

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Speaker 1: Both instruments are producing the same note at the same frequency,

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Speaker 1: assuming both instruments are you know, properly tuned, and both

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Speaker 1: of them are using the same pitch tuning, but you

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Speaker 1: would hear a difference in the type of sound between them, right,

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Speaker 1: A piano and a guitar sound different. Otherwise all instruments

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Speaker 1: would produce exactly the same kind of sound as each other.

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Speaker 1: But you know, you can tell the difference between a

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Speaker 1: piano and a guitar, or a clarinet or a flute

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Speaker 1: or whatever. The tambre is different, even if the instruments

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Speaker 1: are all producing you know, technically the same frequency, even

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Speaker 1: at the same volume. This leads us to the fact

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Speaker 1: that sound is this continuous thing for us. It isn't

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Speaker 1: happening in discrete units. It's kind of like the difference

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Speaker 1: between jumping into a pool filled filled with water, which

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Speaker 1: is you know, continuous to us because we can't you know,

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Speaker 1: experience it down on the molecular level, or jumping into

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Speaker 1: a pool that's filled with plastic balls. So to us,

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Speaker 1: sound is kind of like a fluid, and analog recording

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Speaker 1: captures that. The analog approach to recording is older than digital.

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Speaker 1: So way way back in the nineteenth century, folks like

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Speaker 1: Alexander Graham Bell, we're trying to figure out how to

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Speaker 1: transmit the human voice across great distances using electricity, and

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Speaker 1: the microphone was one half of what was needed to

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Speaker 1: do this, the loud speaker being the other half. And

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Speaker 1: the basic way a standard microphone works is to convert

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Speaker 1: sound that continuous you know phenomena of pressure wave changes

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Speaker 1: to a varying electric signal, an electric signal that has

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Speaker 1: varying voltage. This is another continuous phenomena, right, it's unbroken,

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Speaker 1: it's it's like another wave. Here's how it works. So

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Speaker 1: inside an analog microphone is a tiny little diaphragm, typically

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Speaker 1: made of very thin plastic, and it behaves in a

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Speaker 1: way similar to how our ear drums work in our ears.

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Speaker 1: So when sound, you know, these pressure waves hit that microphone,

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Speaker 1: it moves the diaphragm back and forth, and the diaphragm

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Speaker 1: is actually attached to an electro magnet. A simple microphone

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Speaker 1: could have a permanent magnet inside it, and wrapped around

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Speaker 1: this permanent magnet is a little coil of metal wire

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Speaker 1: that connects to the diaphragm. So the diaphragm moves the coil,

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Speaker 1: which then moves along the length of this permanent magnet.

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Speaker 1: That introduces a fluctuating magnetic field, or rather, you know

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Speaker 1: the effect of a fluctuating magnetic field. The permanent magnets

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Speaker 1: magnetic field is stable, but moving a coil through a

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Speaker 1: magnetic field, it's the same thing as if you were

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Speaker 1: to fluctuate a magnetic field around a you know, non

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Speaker 1: moving coil, you get the same effect. Now, the laws

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Speaker 1: of electromagnetism tell us that if you have a conductive

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Speaker 1: material and it encounters a fluctuating magnetic field, that field

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Speaker 1: will then induce an electric current in the conductive material.

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Speaker 1: So now you've got the microphone producing an electric current,

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Speaker 1: and again the voltage of this current varies depending upon

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Speaker 1: the sound hitting the microphone. That means the microphone is

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Speaker 1: a type of transducer. That's a device that converts one

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Speaker 1: form of energy, in this case acoustic pressure, into another

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Speaker 1: form electric signals. Now, you could send this electric current

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Speaker 1: with varying voltage somewhere to do something else interesting, like

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Speaker 1: you could have it go directly to allowed speaker for playback. Now,

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Speaker 1: of course, this electric current is really uh there are

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Speaker 1: you know, very small elements in your microphone, right, so

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Speaker 1: it cannot produce an incredibly strong electric current. So typically

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Speaker 1: you would first pass this electric current through an amplifier,

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Speaker 1: which increases the strength of the signal. I'm not going

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Speaker 1: to go into how amplifiers work. I've talked about in

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Speaker 1: other episodes, and it would mean that this this episode

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Speaker 1: would go like an hour and a half long if

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Speaker 1: I were to to dive into that. The important thing

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Speaker 1: to think of is that amplifiers take incoming week signals

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Speaker 1: and then push out a stronger version of that same signal.

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Speaker 1: Assuming the amplifiers working properly, then that signal could go

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Speaker 1: to a speaker and you would have the same process

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Speaker 1: that you had with the microphone, only in reverse. The

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Speaker 1: speaker also has a voice coil inside it, a coil

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Speaker 1: of you know, conductive of metal wire, and also a

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Speaker 1: magnet inside the loudspeaker. So the incoming current goes to

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Speaker 1: the wire, and we know by the laws of electro

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Speaker 1: magnetism that this means the flowing current through the wire

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Speaker 1: will also produce a magnetic field. I mean, this is

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Speaker 1: how electro magnetism works, and that this magnetic field will

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Speaker 1: then pull and push against the magnetic field generated by

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Speaker 1: the permanent magnet that's already inside the speaker, and this

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Speaker 1: in turn creates the force that pushes and pulls the

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Speaker 1: cone inside the speaker that connects to another diaphragm. This

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Speaker 1: is a much larger diaphragm than the one that's on

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Speaker 1: the microphone on the other side. Right, Because you've boosted

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Speaker 1: the electric signal, it can then have enough power to

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Speaker 1: move this larger diaphragm. So this larger diaphragm begins to

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Speaker 1: move in and out, and it's pushing and pulling air

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Speaker 1: and it's just recreating the acoustic pressure waves that we're

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Speaker 1: used to go into the microphone and generate the electric

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Speaker 1: signal in the first place, so you're kind of preserved

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Speaker 1: this experience from sound going into a microphone. The microphone

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Speaker 1: as a transducer, transforming that acoustic pressure into an electric

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Speaker 1: current with varying voltage, sending that to an amplifier, and

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Speaker 1: then a speaker, which then does the opposite. It's also

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Speaker 1: a transducer. It takes this electric current with varying voltage

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Speaker 1: and converts it back into acoustic pressure and we get

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Speaker 1: the playback. That's an analog chain from start to finish. Now,

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Speaker 1: if you've got a good quality microphone and a good

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Speaker 1: amplifier and a good speaker, you can transmit sound pretty effectively.

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Speaker 1: And because the whole process is using that continuous and

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Speaker 1: varying signal, it is analogous to the experience of hearing

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Speaker 1: the sound itself. We've transformed the energy from one kind

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Speaker 1: to another, but apart from that, it is an unbroken chain. Now,

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Speaker 1: analog media includes stuff like magnetic tape and vinyl records,

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Speaker 1: which are produced in a way where you are transmitting

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Speaker 1: analog signals and they are effectively carved into a surface

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Speaker 1: that then can be picked up with a stylus on

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Speaker 1: a turntable and then converted back into an electric signal

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Speaker 1: that then can be sent to speakers. So either way

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Speaker 1: you are preserving that analog signal with magnetic tape. You've

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Speaker 1: got a recording device set up that takes that varying

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Speaker 1: electric signal from the recording and then creates a magnetic

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Speaker 1: field with the the writer the right head. Uh, And

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Speaker 1: you've got a little electro magnet in this thing, and

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Speaker 1: that magnetic field rearranges particles that aren't a strip of

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Speaker 1: plastic tape. That's how cassette tapes work. That's all VHS

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Speaker 1: tapes work. So attached to this strip of plastic that

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Speaker 1: is the actual tape in a tape, are these tiny

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Speaker 1: magnetic particles that are bound to that plastic. And by

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Speaker 1: applying the magnetic field to the tape, using in a

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Speaker 1: tiny electro magnet, you can change the direction that these

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Speaker 1: particles are facing on the tape itself. So this process

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Speaker 1: arranges particles on magnetic tape in a specific way to

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Speaker 1: record that original electric signal you were using. The magnetic

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Speaker 1: particles represent the original signal and then in turn represents

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Speaker 1: the sound that was used to generate the electric signal

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Speaker 1: during the recording process. So when you play a tape back,

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Speaker 1: the tape passes underneath an electro magnet at a distance

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Speaker 1: that's close enough that the electro magnet is picking up

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Speaker 1: the magnetic fields of all those tiny particles, and the

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Speaker 1: particles have been arranged in patterns because of that, you know,

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Speaker 1: recording process, right. So the fluctuating magnetic field that is

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Speaker 1: created because these particles are now passing by an electro

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Speaker 1: magnet are again reversing that process. The electro magnet starts

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Speaker 1: to generate an electric signal because of that magnetic field,

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Speaker 1: and then can go to an amplifier and then go

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Speaker 1: out to speakers. So again we use a lot of

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Speaker 1: transformational processes to record this sound, right, because you're in

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Speaker 1: this case, we took pressure waves, vibrations, The sound went

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Speaker 1: into a microphone, creates an electric current with varying voltage.

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Speaker 1: That electric current then goes to a tape recorder essentially

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Speaker 1: that uses magnetic fields to record onto tape. We take

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Speaker 1: that tape, we put that tape into a tape player,

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Speaker 1: and that magnetic record then produces an electric current in

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Speaker 1: our tape player, which goes to an amplifier and then

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Speaker 1: goes to drive speakers and replicate the sound that we

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Speaker 1: record in the first place. So again we transformed things

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Speaker 1: multiple times, but the analogous sound process has remained stable. Now,

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Speaker 1: there's a lot in this process that I have not covered.

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Speaker 1: The equipment and methods you use in recording and playback

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Speaker 1: determine whether or not the copy you have is a

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Speaker 1: really like accurate representation of the original sound like does

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Speaker 1: it sound like you were actually there? Or is the

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Speaker 1: nuance lost? And the same is true for a back.

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Speaker 1: Playback on a really sophisticated system will likely sound better

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Speaker 1: than one that's played on some super cheap stereo. Though

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Speaker 1: pretty quickly you do reach a point where the returns

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Speaker 1: are harder to detect, right like where you might listen

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Speaker 1: to something on a good system, and then you might

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Speaker 1: listen to that same thing on what's considered like the

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Speaker 1: highest of high end systems, and you might not be

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Speaker 1: able to tell a whole lot of difference. But the

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Speaker 1: basics for analog recording and playback are all there. Now.

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Speaker 1: When we come back, we'll talk about the digital approach,

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Speaker 1: but first let's take a quick break. Okay, So now,

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Speaker 1: we've got an idea of how the analog process of

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Speaker 1: recording and playback works. We transform stuff, but we still

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Speaker 1: have a continuous signal that represents sound, which is, you know,

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Speaker 1: a continuous phenomena as sound changes, as the pitch and

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Speaker 1: the frequency shifts, or as the volume changes, or as

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Speaker 1: different instruments or voices produced sounds. All those subtle and

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Speaker 1: maybe not so subtle shifts are part of that recording method.

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Speaker 1: It's an unbroken wave. Digital recording uses a different approach

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Speaker 1: in a way. Digital recording is like taking snapshots of

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Speaker 1: what is going on during a recording session. And I

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Speaker 1: thought of a kind of goofy analogy to sort of

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Speaker 1: explain what I mean. So imagine for a moment that

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Speaker 1: you are in a soundproofed room and you cannot hear

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Speaker 1: anything that's going on outside of this room. However, you

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Speaker 1: do have a little panel like almost like a hatch

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Speaker 1: in this room, and it happens to be facing a

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Speaker 1: really big orchestra pit, and the orchestra is playing. And

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Speaker 1: you know this because there's a light in the room

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Speaker 1: that lights up when the orchestra is playing. But you

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Speaker 1: can't hear anything because the rooms sound proved However, next

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Speaker 1: to the panel is a button, and if you press

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Speaker 1: the button, the panel opens up, but only for a

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Speaker 1: split second. Next to the panel, you have a table,

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Speaker 1: you get some paper, you got a pen, and your

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Speaker 1: job is to press the button, listen for that split second,

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Speaker 1: and then write down what you think is going on

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Speaker 1: in the orchestra. You know, like you could write down

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Speaker 1: everything from the specific instruments that you're hearing, the relative

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Speaker 1: volume of those instruments, any sort of harmonies you're hearing.

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Speaker 1: Maybe you're even just trying to play name that tune. Now,

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Speaker 1: let's say there's some other rules in place too. If

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Speaker 1: you push the button, you are not allowed to push

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Speaker 1: it again until five seconds have passed. So every five

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Speaker 1: seconds you get another instant of sound as the panel

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Speaker 1: opens and closes. This is that little snapshot of what's happening.

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Speaker 1: It would be really hard to accurately describe the music

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Speaker 1: because you wouldn't have a lot of information to go by, right,

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Speaker 1: you would just have this instant of sound every five seconds.

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Speaker 1: It might as well be noise at that point. But

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Speaker 1: then let's say we start to decrease the delay, where

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Speaker 1: you get to have the panel open so that you're

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Speaker 1: getting these instants is of sound more close together. As

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Speaker 1: that gets closer and closer, it will start to sound

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Speaker 1: more like uninterrupted music. Maybe we even rig up the button.

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Speaker 1: We tape down the button so it's always pressed down,

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Speaker 1: and the panel still has to open and close, but

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Speaker 1: it can open immediately after it shuts, so it's effectively

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Speaker 1: a shutter. At a fast enough rate, you wouldn't necessarily

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Speaker 1: even notice the shutters effect on the music. To you.

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Speaker 1: It would sound unbroken if it were fast enough, And

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Speaker 1: then you could accurately describe the music you could write down,

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Speaker 1: you know, depending on how quickly you can write, you

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Speaker 1: can write down a really accurate explanation of what is

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Speaker 1: going on with the music, or maybe you're just identifying

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Speaker 1: what pieces playing. But uh, you know, in this case,

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Speaker 1: if you've got that shutter going at a high enough rate,

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Speaker 1: it's almost like you're not in a soundproof room at all. Well,

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Speaker 1: this kind of is how digital recording works. So rather

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Speaker 1: than preserving an unbroken sign Knoll, the digital process breaks

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Speaker 1: up a signal into discrete units. It has to because digital,

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Speaker 1: when we get down to it, we're talking about binary

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Speaker 1: data zeros and ones. You cannot use zeros and ones

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Speaker 1: to uh to to do anything other than talk about

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Speaker 1: discrete units. It can't be a continuous thing. Now. As

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Speaker 1: I mentioned earlier in this episode, there are a lot

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Speaker 1: of quantifiable elements we can look at when it comes

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Speaker 1: to sound. We can describe how loud it is, or

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Speaker 1: what frequency or pitch it is. We can describe the

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Speaker 1: timbre or quality of the sound. That that kind of

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Speaker 1: gets us into areas that are a little less concrete

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Speaker 1: at least in human language and digital equipment like computers

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Speaker 1: are pretty good at handling things that are discrete and quantifiable.

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Speaker 1: This is the realm of computers. And remember, ultimately computers

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Speaker 1: are relying on those zeros and ones to describe everything.

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Speaker 1: Just to be clear, to get to this point, we

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Speaker 1: would need to use an analog to digital converter, but

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Speaker 1: I'm actually gonna circle round back to that later on.

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Speaker 1: For now, we're just going to focus on the basics

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Speaker 1: of digital recording because understanding that makes the whole you know,

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Speaker 1: a D C and d a C stuff way more

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Speaker 1: easy to understand. So, the way digital recording systems work

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Speaker 1: is that they take snapshots of a continuous wave. They're

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Speaker 1: measuring precisely all the elements at that moment in time

403
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Speaker 1: of the wave and it's or signal signal is probably

404
00:24:33,359 --> 00:24:35,760
Speaker 1: a better word than wave. Really, we're talking about the

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Speaker 1: electric signal generated as you're using a transducer to pick

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Speaker 1: up sound from you know, wherever. So in this way,

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Speaker 1: they're like that panel in that soundproof room. If the

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Speaker 1: sample rate is too low, if you are not sampling

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Speaker 1: the signal frequently enough, then you do not get an

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Speaker 1: accurate representation of that original signal. You know, You're you're

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Speaker 1: having to make a lot of guesses of what's happening

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Speaker 1: between each snapshot. Just like if you had a camera

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Speaker 1: and you were taking pictures of a fast moving, you know,

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Speaker 1: scenario in front of you. If the rate of which

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Speaker 1: you're taking pictures is pretty slow, you've got to make

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Speaker 1: a lot of interpretation of what happened between picture one

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Speaker 1: and picture two, and picture two in picture three. Same

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Speaker 1: thing with these digital recording systems. If you were to

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Speaker 1: try and play a recording like that back, it would

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Speaker 1: not sound very good because it would not be a

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Speaker 1: good representation of the original signal. So you need a

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Speaker 1: really fast sample rate to get an accurate representation of

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Speaker 1: what was really happening. This is the major difference between

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Speaker 1: analog and digital. Analog is continuous and unbroken. Digital is discreet.

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Speaker 1: But if you are using a very fast sample rate,

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Speaker 1: you can create a digital record of a continuous signal

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Speaker 1: that to human ears appears to be continuous itself. Again,

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Speaker 1: if that shutter is opening and closing fast enough, it's

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Speaker 1: almost like it's not even there. Now, let's imagine that

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Speaker 1: we've got two graphs that are showing the same signal, right,

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Speaker 1: and on the left side we've got the analog signal represented,

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Speaker 1: and on the rights that we've got the digital signal. Now,

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00:26:22,080 --> 00:26:25,080
Speaker 1: let's say at first glance, these two are identical. They

434
00:26:25,080 --> 00:26:27,960
Speaker 1: both look like, you know, a typical sign wave. But

435
00:26:28,000 --> 00:26:31,080
Speaker 1: then you zoom into the analog representation. But no matter

436
00:26:31,119 --> 00:26:33,000
Speaker 1: how how far you zoom in, you see it's just

437
00:26:33,040 --> 00:26:39,120
Speaker 1: a continuous, unbroken line that's representing this this signe wave. Now,

438
00:26:39,200 --> 00:26:41,920
Speaker 1: let's say we take the digital one and we zoom

439
00:26:41,960 --> 00:26:43,760
Speaker 1: way in. Well, as we zoom wag in, we and

440
00:26:43,840 --> 00:26:46,639
Speaker 1: we get closer, we see that rather than being continuous,

441
00:26:46,680 --> 00:26:50,679
Speaker 1: it's actually a series of discrete moments, like almost like

442
00:26:50,760 --> 00:26:54,880
Speaker 1: steps or stairs. That's kind of what we're talking about here.

443
00:26:54,920 --> 00:26:57,640
Speaker 1: The question is how many stairs do we use? Like,

444
00:26:57,680 --> 00:27:01,960
Speaker 1: what's the resolution that we're using here. You can kind

445
00:27:01,960 --> 00:27:05,280
Speaker 1: of think of it like megapixels in a picture. If

446
00:27:05,280 --> 00:27:08,080
Speaker 1: you don't have a lot of megapixels, then you might

447
00:27:08,160 --> 00:27:11,480
Speaker 1: see some blockiness in a photo once you get to

448
00:27:11,520 --> 00:27:15,359
Speaker 1: a certain density, depending on you know, the size of

449
00:27:15,400 --> 00:27:17,440
Speaker 1: the image you're looking at, Like if you're looking at

450
00:27:17,520 --> 00:27:19,119
Speaker 1: on the side of a building, you're gonna need a

451
00:27:19,160 --> 00:27:22,240
Speaker 1: lot of megapixels so it doesn't look blocky. But depending

452
00:27:22,280 --> 00:27:25,200
Speaker 1: on that, uh, it may look really smooth. Same sort

453
00:27:25,240 --> 00:27:28,359
Speaker 1: of thing with sound. Now, if you've ever played with

454
00:27:28,480 --> 00:27:33,440
Speaker 1: digital audio recorders, you've probably seen something labeled sample rate

455
00:27:33,760 --> 00:27:37,320
Speaker 1: or project rate. This refers to the number of samples

456
00:27:37,359 --> 00:27:41,000
Speaker 1: that the recording is taking every second, and to record

457
00:27:41,000 --> 00:27:43,840
Speaker 1: a sound, that sample rate has to be fast enough

458
00:27:44,080 --> 00:27:48,600
Speaker 1: to take two samples within one wavelength of every sound

459
00:27:48,680 --> 00:27:52,359
Speaker 1: that's appearing in that in that recording. And remember I

460
00:27:52,400 --> 00:27:56,520
Speaker 1: said that it sounds wavelength is inversely proportional or has

461
00:27:56,560 --> 00:28:00,919
Speaker 1: an inverse proportional relationship to the sounds frequency, So the

462
00:28:01,040 --> 00:28:05,240
Speaker 1: higher frequency sounds have shorter wavelengths, and you do need

463
00:28:05,320 --> 00:28:09,320
Speaker 1: two samples per wavelength to capture the data necessary. To

464
00:28:09,440 --> 00:28:12,040
Speaker 1: have a recording of that sound. If the wavelength is

465
00:28:12,080 --> 00:28:15,080
Speaker 1: too small, then your sample rate will not be sufficient

466
00:28:15,119 --> 00:28:17,680
Speaker 1: to get all, you know, the full information about that

467
00:28:17,880 --> 00:28:20,080
Speaker 1: sound wave. You won't be able to record it, at

468
00:28:20,119 --> 00:28:23,960
Speaker 1: least not accurately. So remember I said the typical range

469
00:28:23,960 --> 00:28:27,840
Speaker 1: of human hearing is between twenty hurts to twenty killer

470
00:28:27,920 --> 00:28:32,399
Speaker 1: hurts or twenty thousand hurts. That's twenty thousand cycles per second,

471
00:28:32,720 --> 00:28:35,040
Speaker 1: and you have to gather two samples per wavelength or

472
00:28:35,320 --> 00:28:37,760
Speaker 1: or cycle. So that means you need a sampling rate

473
00:28:37,800 --> 00:28:41,040
Speaker 1: of at least forty thousand times per second or forty

474
00:28:41,160 --> 00:28:44,040
Speaker 1: killer hurts to be able to sample everything that's within

475
00:28:44,120 --> 00:28:48,000
Speaker 1: the typical human hearing range. Well, a basic sample rate

476
00:28:48,040 --> 00:28:50,960
Speaker 1: that a lot of people will use for various recording

477
00:28:50,960 --> 00:28:55,160
Speaker 1: projects is forty four point one killer hurts, uh, and

478
00:28:55,200 --> 00:28:57,000
Speaker 1: then they go up from there. In fact, we use

479
00:28:57,200 --> 00:29:00,600
Speaker 1: forty eight killer hurts when we're recording our episodes. I'm

480
00:29:00,680 --> 00:29:02,880
Speaker 1: using forty eight killer hurts right now. I had to

481
00:29:02,960 --> 00:29:06,280
Speaker 1: check because I did accidentally do forty four point one

482
00:29:06,360 --> 00:29:09,600
Speaker 1: for an episode a few weeks back, and Sary needed

483
00:29:09,600 --> 00:29:12,040
Speaker 1: to gently remind me that I need to fix that.

484
00:29:12,480 --> 00:29:14,960
Speaker 1: So it's a forty eight Killer Hurts. So I also

485
00:29:15,040 --> 00:29:18,719
Speaker 1: mentioned that we're quantifying all those elements about the sound.

486
00:29:18,800 --> 00:29:22,440
Speaker 1: We want as accurate a representation of the original sound

487
00:29:23,120 --> 00:29:25,920
Speaker 1: as possible. That means we're not just concerned with the

488
00:29:26,040 --> 00:29:30,000
Speaker 1: number of snapshots that we're taking every second. We're also

489
00:29:30,160 --> 00:29:33,960
Speaker 1: concerned with the quality of each of those snapshots. If

490
00:29:34,000 --> 00:29:36,640
Speaker 1: we were using a literal camera to take pictures, we

491
00:29:36,680 --> 00:29:38,880
Speaker 1: would want stuff like the lighting and the lens to

492
00:29:38,920 --> 00:29:42,080
Speaker 1: be perfect so that every single photo we got was

493
00:29:42,120 --> 00:29:45,320
Speaker 1: an accurate representation of what we were seeing when we

494
00:29:45,320 --> 00:29:48,400
Speaker 1: were there. Well, with digital recording, you know, we're not

495
00:29:48,440 --> 00:29:51,120
Speaker 1: talking about lights and cameras. We're talking about how much

496
00:29:51,280 --> 00:29:55,760
Speaker 1: data we're using to describe the original signal. This is

497
00:29:55,800 --> 00:29:59,920
Speaker 1: called bit depth. Bit depth refers to how many potential

498
00:30:00,160 --> 00:30:03,440
Speaker 1: values we can assign to a signal in an effort

499
00:30:03,480 --> 00:30:06,480
Speaker 1: to describe it. The more potential values we can use,

500
00:30:07,000 --> 00:30:10,640
Speaker 1: the more accurately we can describe the signal. So let's

501
00:30:10,640 --> 00:30:13,920
Speaker 1: do another analogy. All right, Let's say that we're in

502
00:30:13,960 --> 00:30:16,960
Speaker 1: a room. It's you and your best friend. Your best

503
00:30:16,960 --> 00:30:18,920
Speaker 1: friends all the way, I across the other side of

504
00:30:18,920 --> 00:30:22,800
Speaker 1: the room, and I hand you a picture. Your job

505
00:30:23,040 --> 00:30:26,520
Speaker 1: is to subscribe that picture to your best friend who's

506
00:30:26,520 --> 00:30:28,959
Speaker 1: across the room. Your best friend cannot see the picture,

507
00:30:29,280 --> 00:30:32,400
Speaker 1: They can only hear your description. Their job is to

508
00:30:32,480 --> 00:30:35,520
Speaker 1: try and recreate the picture, to draw it as you

509
00:30:35,560 --> 00:30:38,640
Speaker 1: describe it. However, I give you some more restrictions. I say,

510
00:30:39,080 --> 00:30:42,040
Speaker 1: you can only use five adjectives. Uh, you can only

511
00:30:42,120 --> 00:30:44,720
Speaker 1: use five sentences, and they have to be simple sentences.

512
00:30:44,720 --> 00:30:48,680
Speaker 1: They can't be compound or complex or anything. Five simple

513
00:30:48,720 --> 00:30:52,680
Speaker 1: short sentences with a maximum of five adjectives to describe

514
00:30:52,720 --> 00:30:56,720
Speaker 1: that picture. Well, chances are your best friend would draw

515
00:30:56,760 --> 00:30:59,800
Speaker 1: something that's kind of similar to the picture I gave you,

516
00:31:00,360 --> 00:31:02,840
Speaker 1: but it wouldn't be an accurate copy of it. Right.

517
00:31:02,960 --> 00:31:04,480
Speaker 1: You might be like, Okay, I can see where you

518
00:31:04,560 --> 00:31:07,520
Speaker 1: got that based upon the description. But let's say we

519
00:31:07,560 --> 00:31:09,880
Speaker 1: repeat this task, and each time we repeat it, I

520
00:31:09,960 --> 00:31:12,320
Speaker 1: give you a little more freedom and how you can

521
00:31:12,360 --> 00:31:15,320
Speaker 1: describe the picture you're looking at to your friend. So

522
00:31:15,360 --> 00:31:17,760
Speaker 1: you get to use more adjectives, you get to use

523
00:31:17,760 --> 00:31:21,280
Speaker 1: more complex sentences, and each time you're given a larger

524
00:31:21,360 --> 00:31:24,320
Speaker 1: set of potential values that you can express to your

525
00:31:24,360 --> 00:31:27,880
Speaker 1: best friend. Well, that's kind of like bit depths. If

526
00:31:27,880 --> 00:31:32,080
Speaker 1: you're using sixteen bit bit depths. That means you're using

527
00:31:32,120 --> 00:31:35,239
Speaker 1: sixteen bits to determine the range of values that can

528
00:31:35,320 --> 00:31:39,040
Speaker 1: describe the signal. So a bit is either a zero

529
00:31:39,160 --> 00:31:42,160
Speaker 1: or a one. With sixteen bits, you can represent up

530
00:31:42,160 --> 00:31:46,400
Speaker 1: to sixty five thousand, five hundred thirty six values. However,

531
00:31:46,880 --> 00:31:48,640
Speaker 1: let's say you were to go to thirty two bit,

532
00:31:48,960 --> 00:31:52,680
Speaker 1: so sixteen to thirty two, you would think, oh, you

533
00:31:52,680 --> 00:31:55,400
Speaker 1: could do twice as many. That's not that's not the case.

534
00:31:55,640 --> 00:31:58,160
Speaker 1: With thirty two bit depth, you wouldn't be talking about

535
00:31:58,200 --> 00:32:01,040
Speaker 1: twice as many as sixteen bit. With thirty two bits,

536
00:32:01,080 --> 00:32:04,040
Speaker 1: you would be able to describe up to four billion,

537
00:32:04,200 --> 00:32:08,360
Speaker 1: two million, nine d sixty seven thousand, two hundred nineties

538
00:32:08,440 --> 00:32:12,400
Speaker 1: six values. So the greater the bit depth, the more

539
00:32:12,560 --> 00:32:18,960
Speaker 1: accurately you can describe something. Essentially, uh So, it's both

540
00:32:19,000 --> 00:32:22,320
Speaker 1: the sample rate and bit depth together that can allow

541
00:32:22,400 --> 00:32:26,880
Speaker 1: a digital system to create a digital recording that represents

542
00:32:27,320 --> 00:32:32,160
Speaker 1: that continuous signal. It was sampling. Again, the digital recording

543
00:32:32,520 --> 00:32:35,200
Speaker 1: is not continuous. If we zoomed way in, we would

544
00:32:35,200 --> 00:32:36,959
Speaker 1: see it's a bunch of these little steps that are

545
00:32:37,000 --> 00:32:39,560
Speaker 1: all linked together. But if the sample rate is high

546
00:32:39,680 --> 00:32:42,080
Speaker 1: enough and the bit depth is great, enough, we can

547
00:32:42,120 --> 00:32:44,920
Speaker 1: reach a point where the human ear really can't discern

548
00:32:45,000 --> 00:32:48,800
Speaker 1: the difference. Does this mean at lower settings we would

549
00:32:48,800 --> 00:32:52,640
Speaker 1: actually notice a difference if you go low enough. Yeah,

550
00:32:52,760 --> 00:32:56,120
Speaker 1: but really, most of the time even sixteen bit is

551
00:32:56,160 --> 00:33:00,520
Speaker 1: sufficient for just plain old recording and playback. However, if

552
00:33:00,560 --> 00:33:02,520
Speaker 1: you want to work on a project. Let's say you're

553
00:33:02,840 --> 00:33:06,880
Speaker 1: an editor and you're you're trying to edit together music

554
00:33:06,960 --> 00:33:10,760
Speaker 1: files or audio files, larger bit depth gives you much

555
00:33:10,760 --> 00:33:14,160
Speaker 1: more space to work in without introducing stuff like distortion.

556
00:33:14,680 --> 00:33:18,040
Speaker 1: This is called headroom. And if you remember the character

557
00:33:18,120 --> 00:33:21,440
Speaker 1: Max Headroom, that name is a pun on this very

558
00:33:21,480 --> 00:33:25,880
Speaker 1: sort of thing. Technically, at the lower rates you get

559
00:33:26,000 --> 00:33:30,719
Speaker 1: deviations from the true sound. You're essentially inserting errors into

560
00:33:31,000 --> 00:33:35,160
Speaker 1: the digital file. Uh As you increase semple rate and

561
00:33:35,200 --> 00:33:38,320
Speaker 1: bit depth, you can decrease those errors until you reach

562
00:33:38,360 --> 00:33:42,160
Speaker 1: a point where any errors that exist are are impossible

563
00:33:42,200 --> 00:33:45,840
Speaker 1: to detect, at least with our natural equipment. Maybe you

564
00:33:45,840 --> 00:33:49,720
Speaker 1: could detect them if you had supersensitive electronic equipment to

565
00:33:49,920 --> 00:33:52,680
Speaker 1: indicate it, but it wouldn't be something that would be

566
00:33:52,760 --> 00:33:57,600
Speaker 1: necessarily perceptible to human ears. One other interesting thing, or

567
00:33:57,600 --> 00:34:00,080
Speaker 1: a couple of interesting things that I should mention with

568
00:34:00,120 --> 00:34:03,040
Speaker 1: sample rates. So I said, like, your sample rate has

569
00:34:03,080 --> 00:34:05,360
Speaker 1: to be fast enough to capture two points of data

570
00:34:05,560 --> 00:34:08,279
Speaker 1: along the wavelength of every sound, and for most of us,

571
00:34:08,320 --> 00:34:11,560
Speaker 1: that hearing range caps out at twenty killer hurts. That

572
00:34:11,680 --> 00:34:13,600
Speaker 1: might lead you to the question, well, why would you

573
00:34:13,600 --> 00:34:16,560
Speaker 1: bother to go higher than forty killer hurts? Now, if

574
00:34:16,600 --> 00:34:19,560
Speaker 1: twenty killer hurts is the limit of human hearing, typical

575
00:34:19,600 --> 00:34:23,360
Speaker 1: human hearing, why go to forty four point one? Well,

576
00:34:24,280 --> 00:34:26,160
Speaker 1: there are some other things that we need to think

577
00:34:26,200 --> 00:34:28,960
Speaker 1: about that play a factor in this. One of those

578
00:34:28,960 --> 00:34:33,200
Speaker 1: are harmonics. Uh Now, harmonics are way too complicated for

579
00:34:33,239 --> 00:34:36,120
Speaker 1: me to really fully get into in this episode. But

580
00:34:36,239 --> 00:34:39,800
Speaker 1: harmonics can actually exist above the range of human hearing

581
00:34:40,120 --> 00:34:43,960
Speaker 1: and yet still shape how we experience a sound. You

582
00:34:43,960 --> 00:34:48,319
Speaker 1: can almost think of it as the harmonics are sculpting

583
00:34:49,080 --> 00:34:51,440
Speaker 1: the sounds we hear. So even harmonics that are outside

584
00:34:51,440 --> 00:34:53,880
Speaker 1: of our hearing range might be affecting the sounds we

585
00:34:54,000 --> 00:34:57,640
Speaker 1: still can here. So we're not hearing the harmonics directly,

586
00:34:58,040 --> 00:35:00,960
Speaker 1: We're rather experiencing how they are affecting the rest of

587
00:35:01,000 --> 00:35:04,080
Speaker 1: the stuff we can perceive. If that makes sense, Well,

588
00:35:04,920 --> 00:35:07,279
Speaker 1: if you're sampling at a rate that's too low to

589
00:35:07,360 --> 00:35:10,640
Speaker 1: capture those harmonics. Those harmonics are not going to be

590
00:35:10,680 --> 00:35:13,160
Speaker 1: in the digital recording, so they won't be in the playback.

591
00:35:13,200 --> 00:35:16,279
Speaker 1: When you listen to it, you lose that sound. So

592
00:35:16,560 --> 00:35:19,040
Speaker 1: when you do listen back, you're gonna be losing those

593
00:35:19,080 --> 00:35:23,879
Speaker 1: effects and you're not going to experience the sound as

594
00:35:23,920 --> 00:35:25,840
Speaker 1: you would had you been in the place when it

595
00:35:25,920 --> 00:35:28,839
Speaker 1: was being recorded. Also, one thing that we can do

596
00:35:28,880 --> 00:35:31,600
Speaker 1: with recordings is we can change the pitch when we

597
00:35:31,640 --> 00:35:34,200
Speaker 1: record stuff. You know, like if you have a digital recording,

598
00:35:34,760 --> 00:35:39,319
Speaker 1: you can digitally change the pitch. In fact, Tari, if

599
00:35:39,360 --> 00:35:43,160
Speaker 1: you would like to digitally alter the pitch of my voice,

600
00:35:43,280 --> 00:35:47,479
Speaker 1: maybe increase the pitch so that I get that kind

601
00:35:47,520 --> 00:35:51,160
Speaker 1: of chipmunk sound to it. That's you know, boosting the

602
00:35:51,239 --> 00:35:55,960
Speaker 1: frequency up or maybe bringing that frequency way down and

603
00:35:56,000 --> 00:36:00,759
Speaker 1: giving me that deep, bass, booming voice that I know

604
00:36:00,840 --> 00:36:04,720
Speaker 1: I'll never have and I'll never be able to really

605
00:36:05,160 --> 00:36:10,279
Speaker 1: play like a baritone in a musical. Feel free to

606
00:36:10,320 --> 00:36:13,960
Speaker 1: do it. The world is your plaything. So you can

607
00:36:14,080 --> 00:36:16,759
Speaker 1: record audio with a sample rate of forty four point

608
00:36:16,760 --> 00:36:20,000
Speaker 1: one killer hurts. Then on playback, maybe you decide you

609
00:36:20,040 --> 00:36:23,960
Speaker 1: want to pitch everything down well, you'll hit a ceiling

610
00:36:24,320 --> 00:36:27,319
Speaker 1: of the sounds that you'll have in that recording once

611
00:36:27,320 --> 00:36:29,719
Speaker 1: you get to killer hurts or so. So, if there

612
00:36:29,760 --> 00:36:34,200
Speaker 1: were sounds that were above killer hurts, you're not really

613
00:36:34,200 --> 00:36:36,000
Speaker 1: going to be able to hear them with the pitched

614
00:36:36,080 --> 00:36:39,680
Speaker 1: down recording. Remember that pitch down recording will bring stuff

615
00:36:39,719 --> 00:36:43,200
Speaker 1: that is outside human hearing into the range of human

616
00:36:43,200 --> 00:36:46,000
Speaker 1: hearing because you've pitched it down. But if your sample

617
00:36:46,120 --> 00:36:50,160
Speaker 1: rate is too slow, too low, in other words, you

618
00:36:50,160 --> 00:36:55,040
Speaker 1: won't have captured those higher pitches. So let's say that

619
00:36:55,080 --> 00:36:58,520
Speaker 1: you're recording something that's in a very very high frequency,

620
00:36:58,680 --> 00:37:01,239
Speaker 1: like beyond the range of human hearing. But then you

621
00:37:01,280 --> 00:37:03,799
Speaker 1: want to do a pitch adjustment so that people can

622
00:37:03,840 --> 00:37:08,640
Speaker 1: actually hear a sound, even though you know normally they

623
00:37:08,640 --> 00:37:10,000
Speaker 1: wouldn't be able to hear it at all because it

624
00:37:10,000 --> 00:37:11,759
Speaker 1: would be outside their range. Maybe you're doing like a

625
00:37:11,840 --> 00:37:16,160
Speaker 1: nature documentary and there's a critter that makes sounds that

626
00:37:16,200 --> 00:37:18,600
Speaker 1: typically we cannot hear, but by pitching it down, you

627
00:37:18,600 --> 00:37:20,920
Speaker 1: can say this is what it sounds like once we

628
00:37:20,960 --> 00:37:23,200
Speaker 1: reduce the pitch. Well, you have to have a sample

629
00:37:23,280 --> 00:37:27,520
Speaker 1: rate that's high enough so that you capture that range

630
00:37:27,520 --> 00:37:30,319
Speaker 1: of sound in the first place. Right, So that's one

631
00:37:30,400 --> 00:37:33,120
Speaker 1: reason why you might want a very high sample rate.

632
00:37:34,000 --> 00:37:36,239
Speaker 1: I just thought that was neat all. Right, we need

633
00:37:36,280 --> 00:37:38,799
Speaker 1: to take another break. When we come back, we'll talk

634
00:37:38,840 --> 00:37:40,759
Speaker 1: about the process we need to follow in order to

635
00:37:40,800 --> 00:37:43,799
Speaker 1: go from analog to digital and back again. It's gonna

636
00:37:43,840 --> 00:37:45,160
Speaker 1: be a lot of us talking about some of the

637
00:37:45,160 --> 00:37:47,919
Speaker 1: stuff we just chatted about, and we'll also talk about

638
00:37:48,000 --> 00:37:51,160
Speaker 1: audio files a little bit. But first let's take another

639
00:37:51,239 --> 00:38:03,080
Speaker 1: quick break. Now, before I dive into the converter's part,

640
00:38:03,160 --> 00:38:06,719
Speaker 1: I should add there are some outliers, right There are

641
00:38:06,719 --> 00:38:10,560
Speaker 1: digital microphones. For example. Now there's some digital microphones that

642
00:38:10,719 --> 00:38:13,399
Speaker 1: are analog at the front end, so in other words,

643
00:38:13,440 --> 00:38:16,440
Speaker 1: they still have the diaphragm, they still have the electromagnet,

644
00:38:17,360 --> 00:38:21,560
Speaker 1: they're still generating an electric current with varying voltage. But

645
00:38:21,680 --> 00:38:25,160
Speaker 1: then they'll have an analog to digital converter built into

646
00:38:25,200 --> 00:38:27,400
Speaker 1: the microphone itself. So you have an A D C

647
00:38:28,080 --> 00:38:31,400
Speaker 1: and it's right there in the device, and then you

648
00:38:31,480 --> 00:38:36,480
Speaker 1: have the signal go to other elements of your recording studio.

649
00:38:37,400 --> 00:38:41,720
Speaker 1: There are other digital microphones that use the pressure waves

650
00:38:41,800 --> 00:38:47,600
Speaker 1: to move elements that immediately convert into digital data, getting

651
00:38:47,600 --> 00:38:50,960
Speaker 1: into that is pretty complicated. They are not super common.

652
00:38:51,080 --> 00:38:53,840
Speaker 1: It's not like that's the type of microphone that everyone

653
00:38:53,960 --> 00:38:59,239
Speaker 1: is using. Um, they're important, but you could argue that

654
00:38:59,280 --> 00:39:02,600
Speaker 1: it's a microphon own and a d C all in

655
00:39:02,680 --> 00:39:07,880
Speaker 1: one because you're taking audio, which is an analog you know, signal,

656
00:39:08,840 --> 00:39:12,680
Speaker 1: and you're converting it immediately into binary or digital information.

657
00:39:13,719 --> 00:39:17,320
Speaker 1: But we're really going to talk about analog to digital

658
00:39:17,360 --> 00:39:21,279
Speaker 1: and digital to analog, which is what most equipment is

659
00:39:21,440 --> 00:39:24,360
Speaker 1: dealing with. When we're speaking about this kind of stuff,

660
00:39:24,360 --> 00:39:28,160
Speaker 1: We're not gonna worry about stuff that's native digital because

661
00:39:28,200 --> 00:39:32,600
Speaker 1: it's just it's not that common. Um like digital speakers

662
00:39:33,760 --> 00:39:37,399
Speaker 1: are a different thing altogether as well, and um yeah,

663
00:39:37,480 --> 00:39:40,120
Speaker 1: we're just gonna wipe those out. We're gonna look at

664
00:39:40,160 --> 00:39:42,480
Speaker 1: what most people use, which is that you know, your

665
00:39:42,480 --> 00:39:48,640
Speaker 1: typical stereo system or your typical audio recording setup. So again,

666
00:39:48,719 --> 00:39:53,960
Speaker 1: typically the end equipment that you use to either record

667
00:39:54,080 --> 00:39:56,640
Speaker 1: or listen to audio, the stuff at the very ends

668
00:39:56,640 --> 00:40:01,080
Speaker 1: of that chain are typically analog in nature. Again, there

669
00:40:01,080 --> 00:40:03,600
Speaker 1: are outliers, but for the vast majority of cases, we're

670
00:40:03,640 --> 00:40:08,280
Speaker 1: talking about an analog device that generates an analog signal

671
00:40:08,480 --> 00:40:12,279
Speaker 1: or plays back an analog signal. So we take an

672
00:40:12,280 --> 00:40:15,759
Speaker 1: analog phenomena, the pressure waves that make up sound. We

673
00:40:15,920 --> 00:40:18,960
Speaker 1: feed that through a transducer to create a different but

674
00:40:19,120 --> 00:40:22,080
Speaker 1: still analog signal, in this case, an electric current with

675
00:40:22,200 --> 00:40:25,680
Speaker 1: variable voltage. But now we get to a point where

676
00:40:25,680 --> 00:40:28,000
Speaker 1: we say, all right, we want to transform that into

677
00:40:28,040 --> 00:40:33,800
Speaker 1: a digital file that quantifies this signal. When we play

678
00:40:33,880 --> 00:40:37,400
Speaker 1: the digital file back, that signal ultimately needs to go

679
00:40:37,480 --> 00:40:40,520
Speaker 1: through some kind of loud speaker for us to hear it.

680
00:40:41,160 --> 00:40:44,200
Speaker 1: Maybe that loud speakers in our headphones, maybe it's a

681
00:40:44,239 --> 00:40:47,640
Speaker 1: stereo system, maybe it's you know, the speaker on your smartphone.

682
00:40:48,600 --> 00:40:50,839
Speaker 1: Maybe it's a sound system in a stadium. But we

683
00:40:50,880 --> 00:40:53,759
Speaker 1: need a way to transform that digital information, all those

684
00:40:53,800 --> 00:40:57,560
Speaker 1: zeros and ones into an electric signal with variable voltage,

685
00:40:58,000 --> 00:41:00,640
Speaker 1: and we probably have to amplify that nal so that

686
00:41:00,680 --> 00:41:03,719
Speaker 1: it's strong enough to drive whatever speakers were using to

687
00:41:03,760 --> 00:41:08,360
Speaker 1: create the sound, which again we experience as an analog phenomena. Now,

688
00:41:08,640 --> 00:41:11,520
Speaker 1: if there was some way that we can interface directly

689
00:41:11,600 --> 00:41:15,640
Speaker 1: with machines and have those digital signals interact with our brains,

690
00:41:16,640 --> 00:41:18,879
Speaker 1: maybe we wouldn't need to do this kind of transformation.

691
00:41:19,160 --> 00:41:21,600
Speaker 1: But as it stands we do have to do this,

692
00:41:21,880 --> 00:41:25,520
Speaker 1: and this is where converters come into play. The converters

693
00:41:25,719 --> 00:41:29,839
Speaker 1: could be standalone devices, or frequently they're worked into the

694
00:41:29,880 --> 00:41:34,040
Speaker 1: design of various pieces of equipment. So for example, a

695
00:41:34,160 --> 00:41:36,480
Speaker 1: USB microphone, if you have one of those that you

696
00:41:36,480 --> 00:41:39,040
Speaker 1: plug into your computer, like I'm using one right now

697
00:41:39,080 --> 00:41:44,080
Speaker 1: to record this, they have that a d C converter

698
00:41:44,320 --> 00:41:47,080
Speaker 1: built into them. And that I'm being repetitive because that's

699
00:41:47,160 --> 00:41:50,360
Speaker 1: analog to digital converter. And then I said a DC converter.

700
00:41:50,400 --> 00:41:53,560
Speaker 1: It's like saying a t M machine. Anyway, the microphone

701
00:41:53,600 --> 00:41:56,920
Speaker 1: still acts just as a traditional analog mic on that end,

702
00:41:57,360 --> 00:41:59,279
Speaker 1: but then the electric signal has to go through a

703
00:41:59,280 --> 00:42:02,840
Speaker 1: converter converts into a digital signal, and that's what transmits

704
00:42:02,880 --> 00:42:04,960
Speaker 1: through the USB cable too, you know, whatever you got

705
00:42:04,960 --> 00:42:08,239
Speaker 1: hooked up to, like in my case, it's my work laptop.

706
00:42:08,719 --> 00:42:13,000
Speaker 1: Now here's the thing. There's more than one way to

707
00:42:13,120 --> 00:42:18,319
Speaker 1: convert an analog signal into a digital one. All of

708
00:42:18,360 --> 00:42:22,640
Speaker 1: these ways get pretty technical talking about the way it's sampled,

709
00:42:23,120 --> 00:42:28,439
Speaker 1: the way it ends up taking these measurements of the signal. So,

710
00:42:28,560 --> 00:42:31,200
Speaker 1: for example, with analog to digital converters or a d

711
00:42:31,280 --> 00:42:34,720
Speaker 1: c s. There are several popular methodologies, but generally speaking,

712
00:42:35,600 --> 00:42:39,720
Speaker 1: they all do the same thing on a big picture scale.

713
00:42:39,840 --> 00:42:43,200
Speaker 1: They all sample a signal. This is the snapshots that

714
00:42:43,239 --> 00:42:45,920
Speaker 1: I was talking about earlier. They look at a signal

715
00:42:46,440 --> 00:42:49,680
Speaker 1: and a specific frequency, like a specific They look at

716
00:42:49,680 --> 00:42:52,960
Speaker 1: the signal a specific number of times every second, and

717
00:42:53,000 --> 00:42:57,200
Speaker 1: they quantified the signal. They measure the signal, which determines

718
00:42:57,239 --> 00:43:01,239
Speaker 1: the resolution that you get of the signal. Obviously, if

719
00:43:01,239 --> 00:43:03,640
Speaker 1: you want high quality sound, you need both a good

720
00:43:03,640 --> 00:43:06,759
Speaker 1: sample rate and a good resolution, which we can think

721
00:43:06,800 --> 00:43:09,720
Speaker 1: of as you know, the accuracy in capturing the nature

722
00:43:09,880 --> 00:43:13,120
Speaker 1: of that signal. You can think of it as an

723
00:43:13,120 --> 00:43:17,120
Speaker 1: A d C is measuring the electric current many many

724
00:43:17,160 --> 00:43:21,760
Speaker 1: times per second and quantifies that measurement as digital data.

725
00:43:21,960 --> 00:43:26,960
Speaker 1: And it's not just like how important is this signal

726
00:43:27,080 --> 00:43:30,520
Speaker 1: at this specific moment in time, but also how important

727
00:43:30,560 --> 00:43:35,480
Speaker 1: are the changes in that signal over greater lengths of time. Now,

728
00:43:35,520 --> 00:43:37,680
Speaker 1: the bit depth we can think of is how detailed

729
00:43:37,719 --> 00:43:40,759
Speaker 1: these measurements can be. So the number of measurements and

730
00:43:40,800 --> 00:43:43,759
Speaker 1: the detail we get together determine the quality of the

731
00:43:43,800 --> 00:43:48,240
Speaker 1: digital signal compared to the original analog signal. And again

732
00:43:48,960 --> 00:43:52,760
Speaker 1: we're talking about digitally describing an electric current. At this point,

733
00:43:52,800 --> 00:43:57,680
Speaker 1: we're not talking about describing the sound necessarily. We're describing

734
00:43:57,680 --> 00:44:01,640
Speaker 1: the electric current that the transducer created after the sound

735
00:44:01,760 --> 00:44:04,920
Speaker 1: went through the transducer. Now, if the sample rate of

736
00:44:04,920 --> 00:44:07,359
Speaker 1: an A d C is too low, you get what's

737
00:44:07,360 --> 00:44:10,720
Speaker 1: called alias sing. Now, this means that the digital signal

738
00:44:10,760 --> 00:44:14,680
Speaker 1: will differ greatly from the original signal. Uh. And that

739
00:44:14,760 --> 00:44:16,959
Speaker 1: means that you're not going to have a good representation

740
00:44:17,040 --> 00:44:20,000
Speaker 1: of what was originally creating that signal in the first place,

741
00:44:20,160 --> 00:44:24,560
Speaker 1: in this case, whatever the sound was. UH. So that

742
00:44:24,560 --> 00:44:28,000
Speaker 1: that's what alias sing means in this context. Now, a

743
00:44:28,000 --> 00:44:30,440
Speaker 1: A DOCK or d a C is a digital to

744
00:44:30,480 --> 00:44:33,440
Speaker 1: audio converter, and it's basically the same thing we just

745
00:44:33,480 --> 00:44:37,160
Speaker 1: talked about, but in reverse. The d a C takes

746
00:44:37,480 --> 00:44:42,520
Speaker 1: digital information, which essentially is describing an analog signal an

747
00:44:42,600 --> 00:44:47,840
Speaker 1: electric current of variable voltage. Then it produces that analog signal.

748
00:44:48,760 --> 00:44:51,560
Speaker 1: The way it does again depends upon the type of

749
00:44:51,719 --> 00:44:54,520
Speaker 1: d A C. Just as A d C s have

750
00:44:54,640 --> 00:44:58,400
Speaker 1: different methodologies, so do d a C s. H. I

751
00:44:58,520 --> 00:45:01,239
Speaker 1: might do an episode that goes into more detail, like

752
00:45:01,280 --> 00:45:03,719
Speaker 1: I mentioned at the top of this episode, But honestly,

753
00:45:03,800 --> 00:45:07,719
Speaker 1: once you really start diving in there, it gets incredibly

754
00:45:07,760 --> 00:45:13,200
Speaker 1: technical very quickly. Generally speaking, we're talking about sophisticated circuit

755
00:45:13,239 --> 00:45:16,520
Speaker 1: boards that are designed to convert digital to analog or

756
00:45:16,600 --> 00:45:20,239
Speaker 1: vice versa, to switch between the data made up of

757
00:45:20,360 --> 00:45:25,439
Speaker 1: zeros and ones and a continuous electric signal. And again,

758
00:45:25,480 --> 00:45:27,760
Speaker 1: if there's interest, I'll go into more about how that works,

759
00:45:27,800 --> 00:45:31,200
Speaker 1: but believe me, it gets really complicated, and without visual

760
00:45:31,480 --> 00:45:37,200
Speaker 1: aids it's really hard to kind of get it across. Anyway,

761
00:45:37,440 --> 00:45:40,040
Speaker 1: Now let's talk about audio files. Also, I should mention

762
00:45:40,320 --> 00:45:42,319
Speaker 1: there's a ton of stuff I did not talk about, right,

763
00:45:42,360 --> 00:45:46,600
Speaker 1: I didn't talk about multiplexing or anything like that, So

764
00:45:46,680 --> 00:45:48,600
Speaker 1: there is a lot more to it than just the

765
00:45:49,360 --> 00:45:52,880
Speaker 1: general information I'm giving anyway. Audio files. So, back in

766
00:45:52,920 --> 00:45:56,279
Speaker 1: the day when c d s were fairly new, there

767
00:45:56,280 --> 00:46:00,000
Speaker 1: were audio files who just despised digital media. The process

768
00:46:00,040 --> 00:46:03,880
Speaker 1: us of converting an analog signal into a digital file

769
00:46:04,239 --> 00:46:08,239
Speaker 1: and then back again to analog. Well, that represented a

770
00:46:08,280 --> 00:46:12,080
Speaker 1: potential loss in quality, right, the playback experience might not

771
00:46:12,239 --> 00:46:16,520
Speaker 1: be as vibrant. Audio files typically use words like warm

772
00:46:16,680 --> 00:46:20,080
Speaker 1: or full to describe sound. These are words that are

773
00:46:20,120 --> 00:46:24,719
Speaker 1: hard to quantify they are experiential, I guess, and they

774
00:46:24,719 --> 00:46:29,600
Speaker 1: would lament that digitization removed some of those elements from recordings.

775
00:46:30,239 --> 00:46:33,840
Speaker 1: The thing is, depending upon how you're digitally sampling a signal,

776
00:46:34,000 --> 00:46:37,360
Speaker 1: some of that could be actually happening. You could be

777
00:46:37,480 --> 00:46:40,800
Speaker 1: losing harmonics. And this isn't even touching on the issue

778
00:46:40,840 --> 00:46:43,600
Speaker 1: that you start getting if you're if you're doing stuff

779
00:46:43,640 --> 00:46:47,279
Speaker 1: like compression file compression in this sense. I'll talk about

780
00:46:47,280 --> 00:46:51,400
Speaker 1: audio compression in a bit, but file compression can involve

781
00:46:51,520 --> 00:46:56,239
Speaker 1: using what are called lossy formats. A lossy format discards

782
00:46:56,440 --> 00:47:01,600
Speaker 1: part of a digital file that describe a signal, and

783
00:47:01,719 --> 00:47:04,480
Speaker 1: typically the way it does this is that the encoding

784
00:47:04,600 --> 00:47:08,439
Speaker 1: process is getting rid of information that it deems as

785
00:47:08,480 --> 00:47:13,040
Speaker 1: being irrelevant. So let me explain that last bit. I

786
00:47:13,040 --> 00:47:15,840
Speaker 1: did a full series of episodes about MP three's that

787
00:47:15,920 --> 00:47:18,879
Speaker 1: goes into this into far more detail, but i'll give

788
00:47:18,880 --> 00:47:21,480
Speaker 1: it down in dirty version for this episode. So, the

789
00:47:21,600 --> 00:47:25,880
Speaker 1: MP three method of compressing a file takes a psycho

790
00:47:25,960 --> 00:47:30,200
Speaker 1: acoustic approach in part when figuring out how to make

791
00:47:30,239 --> 00:47:34,920
Speaker 1: an audio file size smaller, because raw audio files can

792
00:47:34,960 --> 00:47:40,000
Speaker 1: be huge if you're really using a very high sample

793
00:47:40,120 --> 00:47:44,040
Speaker 1: rate and a big bit depth. During your recording process,

794
00:47:44,080 --> 00:47:48,680
Speaker 1: you're generating enormous files, right because the system is taking

795
00:47:48,760 --> 00:47:53,160
Speaker 1: data many many many times, many thousands of times every

796
00:47:53,200 --> 00:47:56,480
Speaker 1: second and using an enormous amount of information to try

797
00:47:56,520 --> 00:48:01,879
Speaker 1: and describe that signal each time, every single snapshot. That's

798
00:48:01,920 --> 00:48:05,839
Speaker 1: a lot of information and that isn't really convenient if

799
00:48:05,880 --> 00:48:08,480
Speaker 1: you want to store that file on like an old

800
00:48:08,560 --> 00:48:11,160
Speaker 1: MP three player. You know, if you remember those where

801
00:48:11,200 --> 00:48:13,440
Speaker 1: you had to like in the old old days, you

802
00:48:13,480 --> 00:48:15,839
Speaker 1: had to connect them physically to your computer. You would

803
00:48:15,920 --> 00:48:20,279
Speaker 1: download or rip music and you would then send that

804
00:48:20,680 --> 00:48:24,480
Speaker 1: music file to your device. These devices had very limited

805
00:48:24,760 --> 00:48:27,719
Speaker 1: storage space on them, so you couldn't really hold a

806
00:48:27,719 --> 00:48:30,640
Speaker 1: lot of raw audio. Like a single file might end

807
00:48:30,680 --> 00:48:33,000
Speaker 1: up taking up the entire storage on your m P

808
00:48:33,120 --> 00:48:36,640
Speaker 1: three player. And maybe you really like journeys Don't stop Believing,

809
00:48:36,920 --> 00:48:39,200
Speaker 1: but you might want some other songs on there too.

810
00:48:39,320 --> 00:48:41,120
Speaker 1: This is also more complicated if you want to do

811
00:48:41,200 --> 00:48:45,440
Speaker 1: something like stream music. You don't want to have enormous

812
00:48:45,480 --> 00:48:49,719
Speaker 1: files that would require like a gigabit Internet connection in

813
00:48:49,800 --> 00:48:52,200
Speaker 1: order to be able to stream it, So you have

814
00:48:52,280 --> 00:48:55,040
Speaker 1: to have a way to compress files down to sizes

815
00:48:55,040 --> 00:48:57,879
Speaker 1: that are easier to handle well. The way the MP

816
00:48:58,080 --> 00:49:01,840
Speaker 1: three algorithm does this is that once you set some

817
00:49:01,920 --> 00:49:05,719
Speaker 1: general parameters, like you decide how compressed you want to

818
00:49:05,760 --> 00:49:10,040
Speaker 1: make this file, essentially you're telling the MP three algorithm

819
00:49:10,080 --> 00:49:13,080
Speaker 1: how hard it needs to go. When it's starting to

820
00:49:13,120 --> 00:49:16,239
Speaker 1: cut stuff, well, then the algorithm begins to toss out

821
00:49:16,320 --> 00:49:19,040
Speaker 1: data that, at least in theory, should not affect your

822
00:49:19,120 --> 00:49:23,600
Speaker 1: experience when you listen back to the audio playback. So,

823
00:49:23,640 --> 00:49:27,239
Speaker 1: for example, let's say you've got a sound file and

824
00:49:27,320 --> 00:49:29,960
Speaker 1: in that sound file you have a very soft sound

825
00:49:30,400 --> 00:49:34,000
Speaker 1: that immediately follows a very loud sound. So loud sound happens,

826
00:49:34,239 --> 00:49:38,840
Speaker 1: soft sound happens immediately after that. Well, you wouldn't actually

827
00:49:38,840 --> 00:49:41,719
Speaker 1: hear that really soft sound, and that's just because of

828
00:49:41,719 --> 00:49:46,200
Speaker 1: how our ears work. The loud sound effectively masks the

829
00:49:46,239 --> 00:49:49,120
Speaker 1: softer one, so it's almost like the soft one didn't

830
00:49:49,120 --> 00:49:51,640
Speaker 1: exist at all. Well, if it's like the soft one

831
00:49:51,719 --> 00:49:55,799
Speaker 1: didn't exist, then there's no reason to keep it right.

832
00:49:55,960 --> 00:49:58,920
Speaker 1: If you couldn't hear it anyway, there's no reason that

833
00:49:58,920 --> 00:50:02,719
Speaker 1: that should be in uh the file, right, So the

834
00:50:02,840 --> 00:50:08,080
Speaker 1: algorithm effectively, through analyzing this data says, ah, that doesn't

835
00:50:08,120 --> 00:50:09,879
Speaker 1: need to be in there. No one would hear it,

836
00:50:09,960 --> 00:50:12,560
Speaker 1: so it tosses the data out. That's why it's a

837
00:50:12,640 --> 00:50:17,000
Speaker 1: lossy file format. The same goes for frequencies that would

838
00:50:17,040 --> 00:50:20,439
Speaker 1: be outside the range of human hearing. The logic is, well,

839
00:50:20,480 --> 00:50:24,080
Speaker 1: you can't hear something that's at killer hurts, so we're

840
00:50:24,120 --> 00:50:26,560
Speaker 1: just gonna get rid of anything that's occurring at that

841
00:50:26,600 --> 00:50:31,120
Speaker 1: frequency because there's no reason to keep it. However, depending

842
00:50:31,120 --> 00:50:33,200
Speaker 1: on how much you want to compress that file, those

843
00:50:33,239 --> 00:50:36,920
Speaker 1: cuts can really start to affect the quality of the

844
00:50:37,000 --> 00:50:40,400
Speaker 1: playback audio when you put it back through you know,

845
00:50:40,440 --> 00:50:42,279
Speaker 1: a decode er and you get the audio on the

846
00:50:42,320 --> 00:50:46,719
Speaker 1: other end. By the way, as I mentioned, file compression

847
00:50:46,880 --> 00:50:50,640
Speaker 1: is not the same thing as audio compression. I'll explain

848
00:50:51,239 --> 00:50:53,640
Speaker 1: what I mean by that, but first let's take one

849
00:50:53,760 --> 00:51:04,360
Speaker 1: last break. Okay, before the break, I said that audio

850
00:51:04,400 --> 00:51:06,879
Speaker 1: compression and file compression are two different things. It does

851
00:51:06,960 --> 00:51:10,439
Speaker 1: get confusing, and I myself have been guilty of kind

852
00:51:10,440 --> 00:51:13,960
Speaker 1: of interchanging the words or not clarifying enough while talking

853
00:51:14,000 --> 00:51:18,000
Speaker 1: about compression and uh, thus I have been guilty of

854
00:51:18,040 --> 00:51:21,279
Speaker 1: confusing it even more so. My apologies for that, but

855
00:51:21,360 --> 00:51:25,680
Speaker 1: let's get to it. Audio compression refers to reducing the

856
00:51:25,760 --> 00:51:30,680
Speaker 1: dynamic range of volume in a recording. Uh So, in

857
00:51:30,719 --> 00:51:34,960
Speaker 1: other words, it's about reducing the volume distance between the

858
00:51:35,080 --> 00:51:38,480
Speaker 1: softest sounds and the loudest sounds. Now, this can be

859
00:51:38,520 --> 00:51:41,600
Speaker 1: really important for certain types of recording. I'll give you

860
00:51:41,600 --> 00:51:44,840
Speaker 1: an example that I frequently run into that drives me nuts.

861
00:51:44,880 --> 00:51:48,520
Speaker 1: And this happens a lot with like streaming media for me,

862
00:51:49,120 --> 00:51:52,440
Speaker 1: so movies and television. Have you ever watched like an

863
00:51:52,440 --> 00:51:56,120
Speaker 1: action movie where you can barely hear some of the dialogue,

864
00:51:56,200 --> 00:51:59,160
Speaker 1: especially if people are speaking in like low voices and

865
00:51:59,239 --> 00:52:01,759
Speaker 1: you know they're trying to be secretive or whatever. And

866
00:52:01,800 --> 00:52:03,440
Speaker 1: then so you turn the volume up so that you

867
00:52:03,440 --> 00:52:05,439
Speaker 1: can hear what people are saying. But then the next

868
00:52:05,480 --> 00:52:09,359
Speaker 1: time something explodes, you're worried that you've just destroyed all

869
00:52:09,400 --> 00:52:12,880
Speaker 1: your speakers, or maybe you've caused yourself permanent hearing damage.

870
00:52:13,320 --> 00:52:16,160
Speaker 1: This happens to me all the time, where the softest

871
00:52:16,200 --> 00:52:20,799
Speaker 1: sounds and the loudest sounds are so far apart that

872
00:52:20,880 --> 00:52:24,480
Speaker 1: there's no comfortable volume. I can select where I can

873
00:52:24,480 --> 00:52:28,399
Speaker 1: hear everything and not feel like one I'm missing out

874
00:52:28,440 --> 00:52:31,239
Speaker 1: on some dialogue, or two my neighbors are going to

875
00:52:31,280 --> 00:52:33,919
Speaker 1: come over and complain that I've got my volume turned

876
00:52:34,000 --> 00:52:37,080
Speaker 1: up too loud. So compression in a case like that

877
00:52:37,239 --> 00:52:40,799
Speaker 1: can narrow the gap between the softest parts and the

878
00:52:40,840 --> 00:52:43,840
Speaker 1: loudest parts so that you can find that kind of

879
00:52:43,880 --> 00:52:49,600
Speaker 1: comfortable volume where you can hear everything. However, going overboard

880
00:52:50,000 --> 00:52:53,400
Speaker 1: with audio compression will reduce the dynamic range in a

881
00:52:53,440 --> 00:52:56,399
Speaker 1: recorded piece of audio, and if you do that too much,

882
00:52:56,640 --> 00:52:59,959
Speaker 1: it can make the audio sound flat and uninteresting, where

883
00:53:00,040 --> 00:53:03,000
Speaker 1: everything is just coming out at exactly the same volume.

884
00:53:03,719 --> 00:53:07,120
Speaker 1: If there's no real volume range, then your ears just

885
00:53:07,200 --> 00:53:10,120
Speaker 1: kind of get tired of hearing everything played back at

886
00:53:10,160 --> 00:53:15,719
Speaker 1: that same level. Some digital recordings really suffered from this

887
00:53:16,160 --> 00:53:19,520
Speaker 1: kind of processing, Like there was an era of music

888
00:53:20,000 --> 00:53:24,480
Speaker 1: where audio files in particular were really complaining that everything

889
00:53:24,520 --> 00:53:27,480
Speaker 1: that was being laid down had so much compression in

890
00:53:27,520 --> 00:53:30,920
Speaker 1: it that there was no real dynamic range and audio,

891
00:53:31,920 --> 00:53:34,320
Speaker 1: and it just meant that the music wasn't as interesting,

892
00:53:34,440 --> 00:53:39,640
Speaker 1: like there wasn't there wasn't enough variation, and it makes

893
00:53:39,920 --> 00:53:43,680
Speaker 1: music kind of boring. Uh, it wouldn't matter if you

894
00:53:43,760 --> 00:53:47,600
Speaker 1: had an analog pressing of a digital recording session, because

895
00:53:47,640 --> 00:53:52,160
Speaker 1: analog does not magically fix the problems of the recording process.

896
00:53:52,200 --> 00:53:55,640
Speaker 1: So if you're recording digitally, and then you make a

897
00:53:55,719 --> 00:53:59,839
Speaker 1: vinyl record pressing of that digital recording. I mean, all

898
00:53:59,840 --> 00:54:02,640
Speaker 1: that processing you did on the digital side, that's still

899
00:54:02,680 --> 00:54:06,960
Speaker 1: going to be part of what ends up being recorded

900
00:54:07,000 --> 00:54:11,240
Speaker 1: on the vinyl. It's it's not like vinyl suddenly cures

901
00:54:11,280 --> 00:54:15,280
Speaker 1: all sins of digital. So even if you were to

902
00:54:15,280 --> 00:54:19,400
Speaker 1: to go with analog audio media, you would still have

903
00:54:19,440 --> 00:54:23,920
Speaker 1: the same problems that were introduced in the digital processing. Now,

904
00:54:24,360 --> 00:54:27,040
Speaker 1: this does not mean that all digital to audio is

905
00:54:27,080 --> 00:54:31,560
Speaker 1: inherently flawed. Even if we just look at the analog chain,

906
00:54:31,840 --> 00:54:35,200
Speaker 1: we have to acknowledge that the process of recording in

907
00:54:35,280 --> 00:54:38,880
Speaker 1: playback means, you know, you're taking pressure waves of the

908
00:54:38,880 --> 00:54:42,160
Speaker 1: original sound, you pass them through a system that converts

909
00:54:42,200 --> 00:54:45,520
Speaker 1: those pressure waves into an analog electric signal, and then

910
00:54:45,520 --> 00:54:48,880
Speaker 1: you've got to reverse that process during playback, and stuff

911
00:54:48,960 --> 00:54:52,840
Speaker 1: can happen along that pathway that could affect either the

912
00:54:52,880 --> 00:54:56,600
Speaker 1: recording process or the playback process or both. So, in

913
00:54:56,640 --> 00:55:01,800
Speaker 1: other words, analog does not necessarily mean better, because flaws

914
00:55:01,880 --> 00:55:05,000
Speaker 1: can exist in the analog approach just as they can

915
00:55:05,080 --> 00:55:08,400
Speaker 1: with the digital approach. And there are other elements as well,

916
00:55:08,520 --> 00:55:12,359
Speaker 1: such as low level noise. Analog systems can introduce a

917
00:55:12,400 --> 00:55:17,600
Speaker 1: low level noise into a signal. Digital avoids that. Now,

918
00:55:17,640 --> 00:55:20,200
Speaker 1: that does not mean that digital is better, mind you,

919
00:55:20,280 --> 00:55:23,120
Speaker 1: because there are other ways to reduce and eliminate noise

920
00:55:23,120 --> 00:55:27,239
Speaker 1: and analog systems and digital can introduce other artifacts that

921
00:55:27,360 --> 00:55:30,560
Speaker 1: didn't exist in the original signal, and then that comes

922
00:55:30,560 --> 00:55:34,719
Speaker 1: across as like errors in your playback, Like you might

923
00:55:34,760 --> 00:55:37,480
Speaker 1: hear some weird blip noise and think, what the heck

924
00:55:37,560 --> 00:55:40,840
Speaker 1: was that, and it wasn't necessarily present in the original

925
00:55:40,880 --> 00:55:45,359
Speaker 1: recording session, but was introduced as a digital artifact. This

926
00:55:45,440 --> 00:55:49,359
Speaker 1: is just another example of how one format is not

927
00:55:49,600 --> 00:55:52,839
Speaker 1: necessarily superior to the other. It depends on way too

928
00:55:52,840 --> 00:55:58,040
Speaker 1: many other factors. They're just different. And honestly, I'm fairly

929
00:55:58,160 --> 00:56:02,040
Speaker 1: confident that if you were to do a double blind test,

930
00:56:02,600 --> 00:56:05,840
Speaker 1: and just in case you're unfamiliar with that term, double

931
00:56:05,840 --> 00:56:09,560
Speaker 1: blind is a type of scientific test where neither the

932
00:56:09,600 --> 00:56:12,560
Speaker 1: subject that's going through the test nor the person who

933
00:56:12,680 --> 00:56:18,280
Speaker 1: is in charge of administering the test knows which version

934
00:56:18,480 --> 00:56:20,480
Speaker 1: anyone is getting. So if you have a control group,

935
00:56:20,719 --> 00:56:24,680
Speaker 1: the person administering the test doesn't know if that's a

936
00:56:24,719 --> 00:56:27,920
Speaker 1: control group or if it's an actual test group. That

937
00:56:28,000 --> 00:56:31,240
Speaker 1: they're testing at any given time. That way, the person

938
00:56:31,280 --> 00:56:35,000
Speaker 1: administering the test does not give bias to the person

939
00:56:35,040 --> 00:56:38,040
Speaker 1: who's experiencing the test. The thought is, if I know

940
00:56:38,200 --> 00:56:41,960
Speaker 1: as an administrator, then I might give a tell to

941
00:56:42,080 --> 00:56:44,839
Speaker 1: the test subject. Right. So let's say it's a double

942
00:56:44,840 --> 00:56:47,520
Speaker 1: blind test and the audio files are going into a

943
00:56:47,600 --> 00:56:51,279
Speaker 1: room and they're going to experience the same piece of

944
00:56:51,320 --> 00:56:56,440
Speaker 1: audio recording, but it's over different systems. So like some

945
00:56:56,520 --> 00:57:00,279
Speaker 1: of the pieces of the same stretch of audio are

946
00:57:00,360 --> 00:57:04,120
Speaker 1: analog sources, some of them are digital sources. They might

947
00:57:04,160 --> 00:57:07,520
Speaker 1: even include different like systems like premium systems, like super

948
00:57:07,680 --> 00:57:11,000
Speaker 1: super high end systems that cost maybe upwards of hundreds

949
00:57:11,040 --> 00:57:14,040
Speaker 1: of thousands of dollars, and maybe just on some that

950
00:57:14,040 --> 00:57:18,040
Speaker 1: are really good systems, like you know, they're still expensive.

951
00:57:18,120 --> 00:57:21,280
Speaker 1: Maybe it's a few thousand dollars, but they're not, you know,

952
00:57:21,440 --> 00:57:25,880
Speaker 1: monumentally expensive. My bet is that most audio files would

953
00:57:25,880 --> 00:57:30,200
Speaker 1: have trouble picking out which ones are analog systems versus

954
00:57:30,240 --> 00:57:33,680
Speaker 1: digital systems unless there's some giveaway, like if you hear

955
00:57:33,720 --> 00:57:38,080
Speaker 1: the scratch of a needle hitting record, then that's kind

956
00:57:38,120 --> 00:57:40,400
Speaker 1: of a dead giveaway. But let's say you know you're

957
00:57:40,440 --> 00:57:43,560
Speaker 1: you're talking about, like the highest of high ends. I

958
00:57:43,600 --> 00:57:45,280
Speaker 1: don't think they would be able to tell the difference

959
00:57:45,480 --> 00:57:48,800
Speaker 1: very easily. And that's because our approach to digital processing

960
00:57:48,880 --> 00:57:52,480
Speaker 1: has become sophisticated enough that to our human ears, it's

961
00:57:52,480 --> 00:57:57,840
Speaker 1: pretty close to an analog signal. And that you know. Also,

962
00:57:57,840 --> 00:58:01,000
Speaker 1: I want to mention the returns on those high end

963
00:58:01,080 --> 00:58:05,880
Speaker 1: audio equipment, like the differences that you start to see

964
00:58:05,920 --> 00:58:09,920
Speaker 1: when you're really hitting that upper echelon of audio equipment.

965
00:58:10,280 --> 00:58:13,440
Speaker 1: Some of those returns are so minor that after you

966
00:58:13,480 --> 00:58:17,840
Speaker 1: reach a certain point, they are largely meaningless. Like like,

967
00:58:18,160 --> 00:58:20,720
Speaker 1: as far as perception goes, you wouldn't be able to

968
00:58:20,760 --> 00:58:24,320
Speaker 1: tell the difference. Uh, And for people like me, people

969
00:58:24,360 --> 00:58:27,400
Speaker 1: who have had some hearing loss, it matters even less

970
00:58:27,400 --> 00:58:30,920
Speaker 1: than that, right because like for me, like you could

971
00:58:30,920 --> 00:58:33,760
Speaker 1: almost say, it's like I have an unsophisticated palette, Like

972
00:58:33,920 --> 00:58:36,960
Speaker 1: you could serve me an amazing meal, but I'm not

973
00:58:37,080 --> 00:58:39,920
Speaker 1: likely to notice it being any better than you know,

974
00:58:40,400 --> 00:58:46,360
Speaker 1: a cheeseburger. But again, this is my hypothesis. I I

975
00:58:46,440 --> 00:58:50,760
Speaker 1: believe this is probably true. It is entirely possible, and

976
00:58:50,840 --> 00:58:53,840
Speaker 1: I admit this that if I actually were to conduct

977
00:58:54,040 --> 00:58:56,600
Speaker 1: this kind of study, I might find that I'm totally

978
00:58:56,600 --> 00:58:59,240
Speaker 1: wrong that the audio files are like no, that is

979
00:58:59,280 --> 00:59:02,880
Speaker 1: clearly the premium, and maybe the differences are subtle, but

980
00:59:03,000 --> 00:59:06,120
Speaker 1: maybe they're detectable. Right, It could be that I'm wrong

981
00:59:06,160 --> 00:59:09,120
Speaker 1: about that. Uh. I just think that there gets to

982
00:59:09,160 --> 00:59:14,919
Speaker 1: be a point where people start to buy into a philosophy,

983
00:59:15,080 --> 00:59:20,840
Speaker 1: especially with audio files that isn't necessarily supportable by you know,

984
00:59:21,160 --> 00:59:25,360
Speaker 1: quantifiable evidence. It becomes so subjective that once you remove

985
00:59:25,400 --> 00:59:28,240
Speaker 1: the subjective element, like you remove the ability for them

986
00:59:28,320 --> 00:59:31,040
Speaker 1: to know whether or not they're listening to their preferred

987
00:59:31,080 --> 00:59:35,000
Speaker 1: set up, that it starts to disappear. Maybe I'm wrong

988
00:59:35,040 --> 00:59:38,400
Speaker 1: about that. I don't think I am, But that's the

989
00:59:38,480 --> 00:59:41,560
Speaker 1: overview of analog and digital and why you have to

990
00:59:41,600 --> 00:59:44,200
Speaker 1: have the converters. As I said, to get into specifics

991
00:59:44,640 --> 00:59:47,320
Speaker 1: would take more time, you know, talking about delta Sigma

992
00:59:47,360 --> 00:59:50,640
Speaker 1: processing and that kind of stuff. But if you want it,

993
00:59:51,160 --> 00:59:53,600
Speaker 1: let me know and I will try and put that

994
00:59:53,680 --> 00:59:58,080
Speaker 1: episode together. It will just be far more niche oriented

995
00:59:58,120 --> 01:00:02,000
Speaker 1: than even this one was. B It is a fascinating subject,

996
01:00:02,120 --> 01:00:06,160
Speaker 1: like there's some really cool technology that goes into making

997
01:00:06,200 --> 01:00:10,400
Speaker 1: this all work, and the fact that that technology does

998
01:00:10,480 --> 01:00:12,920
Speaker 1: work and that it has become so sophisticated is why

999
01:00:12,960 --> 01:00:16,880
Speaker 1: I feel pretty confident in saying that with a sufficiently

1000
01:00:16,960 --> 01:00:19,280
Speaker 1: good system, you wouldn't be able to tell the difference.

1001
01:00:19,840 --> 01:00:22,920
Speaker 1: But that's it for this episode. If you would like

1002
01:00:23,080 --> 01:00:25,760
Speaker 1: me to cover any kind of topic, whatever it might

1003
01:00:25,800 --> 01:00:28,200
Speaker 1: be in the tech world, let me know. The best

1004
01:00:28,200 --> 01:00:30,560
Speaker 1: way to get in touch is on Twitter. The handle

1005
01:00:30,600 --> 01:00:34,040
Speaker 1: for the show is tech Stuff h SW and I

1006
01:00:34,040 --> 01:00:37,439
Speaker 1: greatly appreciate it. I'm getting some wonderful suggestions. Really makes

1007
01:00:37,440 --> 01:00:39,640
Speaker 1: my job easier because I know exactly what people want

1008
01:00:39,680 --> 01:00:43,360
Speaker 1: to hear um. So yeah, reach out and let me

1009
01:00:43,360 --> 01:00:46,360
Speaker 1: know what you think and I'll talk to you again

1010
01:00:47,240 --> 01:00:55,160
Speaker 1: really soon. Tech Stuff is an I Heart Radio production.

1011
01:00:55,400 --> 01:00:58,240
Speaker 1: For more podcasts from my Heart Radio, visit the i

1012
01:00:58,320 --> 01:01:01,439
Speaker 1: Heart Radio app, Apple pine Casts, or wherever you listen

1013
01:01:01,480 --> 01:01:02,520
Speaker 1: to your favorite shows.