Let's take a look at a MATLAB Simulink illustration of the 10 band graphic equalizer now. Now, I'm not going to drag you into any of the details of the MATLAB, but I wrote a simple MATLAB file. The first thing that happens is it constructs the coefficients for the filters. Now, there's one thing I have to tell you here, is that this actually, the im, implementation that I'm showing you. Is, is a digital implementation of the same, equivalent Butterworth Octave Filters. And, there's, a whole course of signal processing that one has to go through to be able to construct these filters in the digital domain. But, the, the point is that they have identical performance to the Octave Bandpass Filters that we were just discovering, and so it doesn't matter. That the implementation is digital, frequency response, and the way things sound obviously are exactly the same. So, what this this MATLAB file does first, is it goes through and it constructs the 10 filters for us. And so, what I've plotted here is the magnitude response of the filter versus frequency. Now, where the frequency is normalized to 22,050 Hertz. Now we're I said we're not going to go into the details of digital sound. But that is exactly one half of the sampling rate of an audio CD. An audio CD is 44,100 Hertz, typically. And, half of that is the maximum frequency that is captured by that CD. And so, we're going to normalize our frequency to that. So a frequency of 22,050, then, corresponds to one. One half is 11,000 Hertz, 11 kHz. Now, the first band in the 10 band graphic eq, equalizer is down here at about 31 and a half Hertz. And so here is the signal or the frequency response plotted on a linear frequency access and here it is on a logarithmic axis. So this has the same appearance as the filters that we were drawing a few minutes ago. And so now the program goes through and it constructs all of the successive filters. So there's the next filter at two times this frequency. The next, so this is, 31 and a half, 63,125. There's 250, 500, 1000, 2000, 4000, 8000, and there's 16,000. And notice I had to construct these filters so they actually dropped off, before I got to 22,000 Hertz. So there's a little bit of funny stuff going on in these higher filters. But here is the set of filters that we set up for the graphic equalizer. So you can see them all. Their peaks each one is at twice the frequency of the previous one and on the log access they all line up nicely, they all look similar. And so when I add them all together here is a node linear frequency, access response looks like this. And on the log response and I plotted log of the magnitude so, the, these are, these scales are both linear. This is linear in frequency, linear in magnitude. This is logarithmic in magnitude, and logarithmic in frequency. And so this looks quite flat in the log magnitude response. So, this is the setup for the 10 band graphic equalizer. So now, those filters are all set. And here's something that I, I realize this is a little hard to see. let me make it as big as we can. I can, try to zoom in on things. But what this is, is Simulink is part of MATLAB Programming environment. And it's a graphical interface that lets you just construct signal processing algorithms just by dragging and dropping blocks. So what we do is we read in an audio file and then it goes, that audio that's coming in passes through these ten filters. And so I've defined filters at 31 and a half, 63, 125 all the way up to 16 kHz. So here is the bank of filters. And then I put a gain slider for each one. And so I can turn up and turn down the amount of that frequency band. And so I have a sample [SOUND file here. I want to play and then we'll just play around with these sliders a little bit to hear what it sounds like. Again, you can do this with your own iTunes. So, it's, it's not, you're not going to hear anything here that you can't hear on your own computer. But, I picked an audio file which is a female singer, a guitar, and a base. And so it's nice because it has each of those instruments are occupying quite different regions of the spectrum. And you can see how when you modify different filters, you really pull up or, or attenuate one of those different voices in the recording. So let's run it and listen for a little bit. So I'll start off by making everything flat. So I'll make the gain of each filter 1. And the so I've got a couple of them here that are not one. Now, let's see, I should have set these all to one first, but and that one's outta whack. Okay, so now let's start the recording. And you'll, first you'll hear it just flat. And then we'll just play with different different bands. [MUSIC]. Okay, let's start by turning up the bass a little bit. [MUSIC]. Turn down [MUSIC], turn down all the way, turn down. [MUSIC] Turn down all the bass, [INAUDIBLE]. so let's take it and turn up like 1 kHz. [MUSIC] Turn up a really high one [MUSIC], okay. So that's enough. You can do this with your own iTunes but I, I can just do this for my own amusement for untold hours. Because it's, such a kick to build your own digital filter. And then implement it and listen to it. So, that is the demonstration of the graphic equalizer. And, going back to where we were, that's that. So that concludes the discussion of band pass filters and graphic equalizers. And hopefully now you have an appreciation, deeper appreciation of how the Graphic Equalizer in your iTunes application is set up. And how it works.