Why do some audio analyzers default to pink noise and others to a sine sweep?
They are different acquisition methods, but they’ll both get you there.
- 2 channel input
- Measure in real time with any source material
- Common among live sound engineers
- 1 channel input
- Measure off line with a pre-determined source
- Measure THD
- Get very high SNR
- Characterize decay (T60, C10)
- Common among acousticians, manufacturers, mobile electronics technicians
You don’t want to feed a real-time measurement with a sine sweep because the real-time analyzer wants a broad-band source, which makes the sine sweep look like mostly nothing. It’s like putting unleaded in a diesel engine.
The transcription was automatically generated. Please let me know if you find any errors.
This year, I’ve been getting a lot of questions about why some audio analyzers use pink noise and why some use sine sweeps, and it started around the time that acoustics came out with their M1 P1 platform. And so I think that was kind of what people are looking at is like, hey, wait, we’re used to kind of hearing pink noise when we use things like smart. Why is now the acoustic system using this sweep?
Should I be using a sweep? What’s going on here? And I can go directly to them and maybe I’ll try to make another video later where I talk to Vic or Scott or someone from acoustics and just chat with them about why they made that decision. But I thought it’d be fun to talk to you about just make a short video about just some general places where we see the show up so that people can just including myself, can have a better idea of like maybe why and why some people prefer it and why some platforms prefer it.
So this is the first question. Why does some audio analyzers use pink noise? Isn’t some use a sign sweep?
Yeah, and that’s really the great place to start. And I think, you know, what we’re really talking about here is not which signal should I use?
But what we’re talking about under the hood is what mathematical method is this analyzer using to get the data right. And that’s something that as an end user we don’t care about. We don’t want to get under the hood and know what math it’s using, but that we’re talking about two different types of math that will give us the same answers. So one type of analyzer is designed so that it can use any signal that we want, and that’ll be the dual channel fee.
So it’s source, independent. Right. And then the other type of math works by using a very specific, mathematically predetermined signal.
So a sweep would be a good example of that.
So there they’re just different acquisition methods. And I think as we’re going to see as we go on here today, you know, they’re both going to get you there.
They’re both going to give you that answer.
If you’re just trying to say, hey, you know, I want to see the magnitude and phase of of this loudspeaker, whatever they can both produce that they just use different mathematical methods under the hood to produce that information. Got it.
OK, so would it be fair to call these should we break it down? Is it real time and not in real time or is it real time? And I are mode. What do you think is the better way to distinguish that?
Well, so the real time measurement that is the source independent one. And it’s happening. It’s happening in real time. Like you said, I can be tweaking a delay or it can be tweaked and I’m going to see those results to show up the measurements continuously running. And that’s the one that can use anything that we want for for a source material. Yeah, non real time would, I guess, be a good catchall term for something like a sweep where you say, OK, run it and it goes and then the computer goes and kind of thinks about it.
And then it brings you your answer. Right. And then if you want to tweak that IQ or something, you’ve got to go run that measurement process again. So it’s it’s not happening in real time. In Smart specifically, we call our non real time mode impulse response mode because we’re gathering the impulse response system. But as you know, once we have the impulse response, we can transform that and get get the frequency response. And either way.
So so that’s our name for it. But yeah, I mean, we’re fundamentally distinguishing between measurement that’s happening continuously in real time and a measurement that’s happening once and then it’s sort of a one shot thing. Yeah.
And just to give you an overview of, you know, there are a lot of these audio analyzers like Smart that I think can do both modes. But just to give an overview of some where we where we’re used to seeing them, they are things like when it comes to the real time mode, we’re used to thinking of things like Smart sat live and now we have open sound meter. That is there’s probably some that I’m missing. But then when it comes to the non real time, I immediately think of like RW for our capture, wave, capture or wave tool.
I can’t remember the name. Is there any that I’m missing that you think are common? Oh, I mean, there’s tons of them.
I think another common real time dual channel platform would be Sistan that people are familiar with. There’s a ton of sweat measurement platforms out there. Generally, it’s it’s a sweep or some other period since signal versus the real time dual channel platform. So in terms of the mechanics, the whole point of the real time dual channel platform, like in Smart, like you’re setting up to inputs the measurement in the reference. So we’re just saying, all right, let’s how do these compare?
We can continuously run that comparison.
Whereas when you’re doing something like Remix Wizard, you don’t have the necessity to do that reference channel that loop back. Right. It’s just the computer is going to produce that signal. It’s going to come out and then it’s just going to record it comes back and you could record that sweep to us CD and play it in a different building and record it.
Just bring it back and. So you have are you inputting one signal or are you inputting a pair of signals? Right. And so that’s the fundamental difference here, is that when the dual channel real time, that the whole point of that reference signal is where we don’t need any particular predetermined signal to do this, just give us a copy of it versus the suite measurements that that that sweep is very particularly created by the computer. And so when it comes back in the computer, you can compare that suite to what it knows that it generated and that’s how it produces the measurement data.
And you and rest on Acoustics recently published a great video that I believe is called Pink Noise versus Sweep. Or is that was the title of that one?
Yeah, I think well, it was kind of had a double title, right, because it’s exactly what we’re talking about. A lot of people say I think it’s called like in parentheses or in quotes. It says pink is sweet and then it says real time versus real time versus impulse response. Right.
So people people should watch that one for sure. And I just want that one yesterday. And it’s the same topic. And you do some great demos in there. But I know you have another demo. So before we dive into this demo that you’re going to show us, let’s just talk about some common places where we see these two things show up. So starting with real time mode, a lot of people, especially those of you like you and me and the people watching this video, we are used to seeing most commonly this real time mode using things like smart when we are doing temporary installs, a lot of concert, sound theater, that kind of thing.
Do you think that’s right? And where else do we see real time mode show up?
Yeah, I would I think it’s a fair statement to say that the majority of people doing live sound system optimization work are probably using a real time tool. And I think the reasons for that are obvious. The fact that it happens in real time and the fact that we can use any test signal, we want those to have some real obvious benefits in a production environment. Obviously, a lot of this is about how quickly we can move. And so there are some real benefits there.
Now, you can obviously do this type of work with this measurement platform. I know I have a couple of good friends who used Rumiko. Was it for a long period of time to do this work? And there’s nothing wrong with that. And I want to stress that, that this is all about you using the tool that you’re comfortable with it give you the results that you need. But given the choice, typically I’m trying to move as quick as as quickly as I can.
I’m going to go for a real time measurement platform for for a lot of this type of work where it’s, hey, we want to just measure the response to this sound system so we can put a couple filters in or I need to dial in some delay times or stuff like that. I just want to continue measuring during the show.
Like, you never stop you potentially. Yeah.
Yeah, exactly. I mean, obviously you can’t do sweeps during a show. Right. So so there are there are times when the circumstances in which we’re trying to work are going to dictate a certain workflow, and that would certainly be one of them. Now, if we want to talk about the sweat measurement platforms or what we’ll just call non real time measurement platforms, the really big benefit there is that the analyzer is able to sort of cut through a lot of the sources of noise that come into our measurements.
So we have a couple of different sources of noise in our measurements. Want to just acoustic noise or at the back or my furnace is running right now.
So that would show up my bird screaming in the next room, all of that stuff. Right.
So so that is something that we deal with both in real time and on real time measurements with averaging the more average that pushes that noise down. But there are other sources of noise and sometimes we want to deal with those in our measurement.
So one source of noise comes from the way that the analyzer has to sort of chop up. When we’re talking about real time measurement, we can use anything that we want. It could be music, whatever. We’ve got to feed the analyzer specifically sized chunks of that signal in order for it to do its thing. And so do the process that is involved in that can contribute some noise. So that’s when you get into a period matched stuff where we’re using a signal that’s the same length as the analysers time window.
So it’s smart. That’s pseudo random noise, which we can we can demo or the sweep. The sweep is going to be you set this wavelength in the analyzer. So that eliminates that source of noise and it can bring down the noise, floor the measurement. And then then finally you have non-linearity of the system. Loudspeakers have harmonic distortion. Right. And so when we have all the other sources of noise in our measurement that’s so down low that we don’t really care about it.
But when you’ve stripped back all the other layers, if you’re doing acoustics work right, this is really where the sweet measurement really comes into play, is if it’s your job to do acoustic treatment for a venue. And you really need to characterize that. Talk about the 60, right, I want to measure 60 dB of clean, reverberant decay, I need a really good signal to noise ratio in my measurement. So now we are stripping back those layers of noise and using you know, you’re talking about I’m going to use a really long sweep.
I’m going to do eight averages. So you’re talking about a 30 second acquisition time? Maybe, but you can get down 60, 70 dB into that reverberant and you can get really accurate characterizations of the reverberant qualities of the space. If you’re a contractor, that might be something that you really need to know. So so that is a situation where I’m willing to spend an extra 30 seconds to get this data and get really, really, really good clean measurement data way down on the noise floor.
And that’s that’s a situation where I definitely go to impulse response mode and smart.
And I’m going to I’m going to spend that extra time and I’m going to get these really clean impulse responses. So like you said, it all comes down to what what are we trying to do when we pull it analyser out? And this is something that I know you’ve talked a lot about in your videos. Right. What is the question we’re trying to answer here? What are we seeking to learn? And so if if it’s I just want to enter the system real quick and put some filters in or whatever.
I don’t need 70 dB of signal to noise ratio because that stuff lives up up at the top. And I’m going to get my answer quickly. And that’s great. And if I’m talking to a client about how much acoustic treatment should we order, I want I want to see that.
I want to know what’s going on. 40, 50, 60, dB down. So so that’s kind of different. Different tools for different jobs.
I remember a couple more jobs that we mentioned the last time you and I talked as well. You talked about a manufacturing lab and I was like, oh yeah, they have these specific conditions they would want to know in real time zone could really benefit them. And they want to see harmonic distortion and they’re looking like really close that drivers and they want to get really deep. And so that’s a common one, right?
Yeah, a lot of people I have some friends that work in loudspeaker design and that type of thing, and they’re typically working in way, way more controlled conditions than than we typically. What did it get? Right. So they’re in probably anechoic chamber or really, well, acoustically treated environment. And they are also measuring for a much higher level of of resolution, I’ll say, than than we typically would, because we know that in a venue we move our mic over a foot and we’re going to see a different answer.
When you’re designing a loudspeaker, they really need to see sub dB resolution on a lot of this stuff. And so so, yeah, they’re going to use typically a sweet measurement in a lab and they don’t care about the acquisition time. They’re not at a show. They don’t have a soundtrack coming up. Right. So so they can take longer acquisition time measurements. And the benefit of the sweet measurement is, like you said, because we’re only putting one frequency at a time through the system, we can keep track of what’s coming out and so we can spot that distortion and we can actually separate it out with a sweet measurement.
So if you’ve worked with Rikyu, is there there’s there’s an ability in there to show harmonic distortion over frequency and each each harmonic, whereas in a real time measurement, the distortion is just part of the tonal response of the system. If you’ve looked at I know you’ve done a little work with showing your viewers the noise test and we see what happens when you get distortion in there. The coherence starts to come down. Right. But in a sweet measurement, we can actually separate that distortion out.
And instead of saying it’s part of the tonality of the measurement, it’s part of the tonality at the system, we can actually split that out of the way. And the benefit there to go back to the acoustics work is if you’re trying to measure the acoustics of a space and you accidentally drive the loud speaker into distortion when you’re measuring the space, it’s separated.
It’s OK. So the ability to separate out distortion is maybe something we may or may not want in a real time situation. But it’s definitely something that’s really important in a controlled condition. If we’re studying distortion, I do work with the rental company here in town and after a month of shows, I’ll take all the boxes and I’ll sweep them and I’ll compare those measurements from what they were a month ago. And that’s a really, really great way to spot a blown driver or another problem with a box that needs to be repaired, stuff that you might not hear with just listening to music or listening to pink noise at a restaurant.
It’s like a health checkup. Exactly.
And so so, again, it’s all about that. We have different ways of taking these measurements, depending on what question we’re trying to answer. One thing I was going to add is that last month I did a training for a bunch of mobile electronics technicians, people who are installing high end systems in cars and trucks, and we decided to use Rumiko Wizzard partly because that’s what they were already using and they were familiar with it. But it turned out to be super useful because they don’t have their client standing next to them, waiting for them to get done in a few seconds like they’re working by themselves in the shop.
And if they needed to if the deadline was tomorrow, they could work all night if they needed to. But my point is that it was really useful to be able to take a measurement and then do a lot of things with it offline and has some great abilities to do, like filter modeling and things like that. And it was great to see that on screen before you take action. And we also mentioned being able to do these offline measurements. So it might be difficult in something like a vehicle to like get your impulse and get your signal generator into the input of that thing.
And so you could just burn a CD. If the car is a CD player with the signal that you need, play it back and record it and do your work that way as well.
Yeah, exactly. So for all of the reasons in a in a busy production environment, why a real time measurement is really great. There are some situations where it just is more complicated to do that, like you said. So if you have a friend who works for else. So how do you measure a smart speaker? Right. That’s all controlled control over wi fi and Bluetooth and stuff. You can’t really get an input into there in real time.
So that’s a perfect example of a situation where you’re like, no, I have this prerecorded test signal. I’m going to just play it back through this thing, pull it back to the analyzer and do what I need to do. And so, yeah, sometimes a non real time measurement is is really helpful. Cool.
So can we take a look at your demo and then we’ll talk about how screwed things up?
Yeah, absolutely, man. All right, let me I’m going to share my my screen here. Can we see this?
I see it. Great. All right. So so this is familiar hopefully to a lot of your viewers. We’re just we’re here and smart. We’re in the default transfer function view. I will point out I am using the version eight point five beta. This beta is public. So if you have a smart version IT license, you can go download this right now. And version eight point five stable will be out in a couple of months. And there’s a reason I’ve chosen this to show you this demo on me because we’ve completely overhauled impulse response mode and it’s really significant and it allows me to look at multiple impulse responses at a time, much like we can in real time mode.
So that’s really, really useful for for this demo in particular. We’re going to be comparing stuff. Right. So I’ll start off.
I’ve got a there’s a loudspeaker and a microphone over here in the corner of my office. And I will just start off by just gathering. I’m going to run the real time measurement for a second. Just take the transfer function here. So apologies for the for the noise. And what do I need, my speaker went to sleep on me. All right, now that it’s woken up, OK, so there’s my transfer function measurement. I’m just going to save this.
Great. So, you know, as you see, as soon as I turn it on, as soon as the system comes up, it starts doing its thing right. And so it’s just going to kind of continue on. I am going to activate the setting that we have, which is to stop the generator once I capture the measurement just so we don’t have to listen to the noise. Oh, cool stuff.
Right in the new feature. So. All right.
So we’re going to flip over to impulse response mode. And the way you do that in Smart as either press the ickey on your keyboard or you just have this little impulse button down here.
And you’ll notice that now this looks a lot more like real time mode than it used to, right? I have my data bar and all that stuff, so this is pretty cool. And I’m going to delete some old data so we can have our fresh new folder notice over here on the right.
Same transfer function. Engine looks exactly the same. I got my two single monitors, I got my delayed time, all that stuff. So that’s the same deal. So the three parameters we’re going to look at here are changing the 50 size. Using a longer field, give us a better signal to noise ratio, adding some averages and both of these things, if you’re familiar with Mickey Wizard, these are both parameters that you can control.
You can say, I want to use a longer sweep and I want to use more averages of that sweep. And then the third parameter we’ll look at is what happens when we change the signal type. So let’s start off with the default settings. We’re going to use the same random pink noise that we had before. So this could be music, could be whatever you want it right. It’s still a dual channel measurement. We’re going to use the 16 KFT and no averages.
And I’m just going to fire this thing up and let it go.
So let’s say that is a random 16 zero averages, right? So this is this is a basic it’s a really notice to talk about a third of a second to come through. So that’s three point forty one milliseconds.
So let’s see if we can lower the noise floor here. Let’s try to get a bigger dynamic range. Right. So one thing we can do is use marriage. So let’s go to let’s go to eight averages.
All right. And we go. So now you can see that it’s lowered that that noise floor by a good, what, 12 dB or so. So that’s pretty good. That’s pretty cool. So let’s now go to a different type of signal. So what we’ve been using up to this point is just random pink noise. Like I said, it could be music, could be whatever you wanted.
If we use pseudo random pink noise, what that does is it’s going to match the length of the pink noise signal. It’s going to be repeating signal now. And you can actually hear it repeating. It sounds like like an old Atari game or something. And it’s going to automatically match to the size of the fee they’re using. So when I hit this button that’s going to drop out, that processing that I was talking about, it’s called a Daito and it basically spoonfeed the analyzer, the correct chunk, so to speak.
But if the signal that we’re generating is already the right size, we don’t have to do that. And that eliminates the source of noise in the measurement.
So before you hit play, I just wanted to I think I’m noticing like this is kind of the classic thing you might think of, like popping a balloon and just recording it in a room right until now.
Yeah. So so we’re doing a dual channel version of that. Right. But yes, you can absolutely. If I if I hit this record button here that turns this into a single channel, direct air. So yeah. Then I could hit play pop the balloon or clap and I’m going to see that it’s a very similar thing. Obviously there are some advantages to the dual channel approach and that we can see the popping and clapping is really tough, right.
Because it’s so short, you’re not putting a lot of energy into that room.
So something like a sweep or pink noise test signal, since we can excite the system for a longer period of time, you don’t have to be so loud in the space. It’s easier to get a good level on your measurement and get above that noise floor. Whereas for for a pop or a clap, if you’re playing that through a you know, you’re probably going to have to push that system right to its limits just to get to get enough energy.
So so there are some benefits in doing this, not as a direct impulse. And of course, we are creating the same result. Right. We’re still creating that impulse response here. We’re just doing it with the dual channel method. Does that make sense? Yeah. Thank you. Cool. All right.
So let’s do the same thing. The only thing we set this time is instead of using random pick noise, we’re going to use the super pink noise and I click the drop our data window and that’s going to sink the pink noise signal length to the size of the fees. I’m going to fire that up.
Nobody can hear it over the zoom audio, but it sounds a little bit different. So that’s that’s a pretty big improvement, right? So pseudo random, let’s call it 16 averages. So what we can see is the noise floor is pretty drastically lowered, right, by a good 18 dB or so. And now we can see that the reverberant decay of the room is starting to be exposed.
That’s cool. Remind me, why do we want to lower the noise floor? I think maybe we covered this at the beginning, but at this point I’ve forgotten.
Well, we may we may or may not want to. Right. So if we’re in, we’re going to finish up by comparing this the frequency responses of these to the real time measurement. And we’ll see that they’re going to be pretty much the same. So, again, if you’re just measuring because you want to kupa, you don’t need to bother with lowering the noise floor because that stuff happens way up on the top. You know, our world was the top 10 dB when we’re we’re not doing stuff that’s 70 dB down.
But if we’re acousticians and we want to characterize the reverberance of a space this up here, if I’m hitting the noise floor only only 30 dB down, that doesn’t give me a whole lot to go on. Right. Whereas if I can push that noise floor down, I’m exposing more of the reverberant qualities of the space and I can do that more accurately. So for an example, I can click the T 60 button and I can you know, you see these markers come into play and they don’t really land quite like they’re supposed to.
We’re generating all these acoustic metrics if we’re for an integrator or we’re trying to study the space itself. And what you can see is as we lower that noise floor, you’re going to see these metrics get more accurate and that marker placement will improve. So so the software just can get a cleaner data set to calculate those types of things, basically. Thank you. Yeah, absolutely. So let’s nuts. Let’s now go to a longer fee. OK, so let’s go up to 60 for KFT.
So that’s a little over a second long at forty eight K sample rate. And so we’re talking about one point three seconds it averages. So now we’re talking about nine or 10 seconds to get to get a measurement, but you’re going to see it’s going to lower that, that noise floor even further. All right, so this looks way different than the other ones, right, so it’s longer. Like we said, you can see that it extends this is this the axis down here?
The X axis is in milliseconds. So we can see over here one point three, four K. It’s not a frequency. That’s that’s a time. Right. So we’re over a second long compared to these guys, which are about a third of a second long. But you can also see that, again, that noise floor is significantly lower than it has been.
So we’ve got even more of that reverberant decay exposed. So I’m going to save this. This was a sixty four K at fifty and we did eight averages. And so in in smaller version eight point four, you could do this work, but you can only look at one trace at a time. And so that’s why I’m using eight point five for this demo, is that it’s obviously a little easier to see this stuff when you can overlay them. All right.
So now let’s go for the sweep. Right. Let’s let’s go for for the big guns in the sweep has that additional ability. So we’ve done averaging. We’ve lowered the acoustic noise. We’ve kind of dealt with that. Then we used a period match noise and that allows us to get the data window out of the picture and that that brought the noise down more. And so when we’re this far down, we’re talking about, you know, you can see where we’re below 70 dB down now, pretty far down now.
We can start talking about. But what about the distortion products of the loudspeaker? And so when we go to the sweet measurement, instead of those being mixed into the noise of the measurement, it’s going to separate them out. And so we’re going to see that. So I’m going to pick a little bit of a longer time record because that works a little bit well, better with a sweet measurement. We’re going to select Pink Sweep as our material here.
And notice I’ve got a setting. One thing I’m going to do is turn this down just a little bit because we don’t need it to be as loud the the signal gen level and smart. This is actually a good thing to remember. This is a peak level. So we’re using pink noise simply as a crass factor, about 12 dB.
So the average the arms level is going to be you below the value in this box when we go to a sine wave sine wave as a crass factor of three dB. So that’s going to sound a lot louder even though the level setting looks the same.
Right. And so it’s just it’s I’m going to just pull it back a little bit.
So the box that I’ve checked here is triggered by impulse response. And so when we get into sweep’s, it’s really important that the sweep lines up with the data window of the analyzer or the time record of the analyzer. Excuse me. So we want that the analysers time record to start. And then we wanted to hear the sweep and then we want to leave enough at the end that the reverb in the room can ring out. And then we want to stop listening and then we want to process that.
And so manually synchronizing that, you can really get into some trouble with getting goofy results because you had timing problems. So that setting when I do this, it’s automatically going to trigger the suite for me when I hit play, which is really cool. I don’t have to worry about trying to sync it up. Right. So we can do a three second sweep, two point seven seconds, and I’ve chosen eight averages. So we’re going to watch this thing go for a good thirty seconds, but you’re going to see the additional benefit of doing that.
OK, and know what, let’s let’s not go crazy. Let’s go to four averages here just because people’s time is valuable these days. All right, let’s let’s hit it. Let’s see what it does. Maurice. There we have it, so we’ve lowered that noise floor even further. And so now we’ve got I mean, I’m at I’m at negative dB here, so we’ve really exceeded our original measurement.
The pink the pink one here, it was minus 30 or so, minus thirty three.
So we’ve gotten pretty much a good forty five or fifty additional dB of dynamic range out of this measurement. So I’m going to save this one to make for averages. And it’s a little bit it’s not super easy to see, but if you have a lot of food in your system, these peaks that you’re seeing here at the end. They’re not super clear on this measurement, but those are the separated out impulse responses of the additional distortion products. So you get an IRR for your fundamental, which was the sweet tone that we hear, and then you get a separate IRP for your second harmonic.
And it is it’s pretty neat.
Now, again, on a super good linear loudspeaker, this doesn’t really get you all that much because you don’t have that much distortion in your measurement to begin with.
But if it’s a if it’s a speaker that’s working really hard toward it, toward the limits of what it can do, you have high distortion in there. This can get you like we see we gained an extra 60 or so by doing this. And so now I can come in and I can hit my 60 button. You see, now those markers really fall along the deck where they need to. And I can come in here and I can look at if I’m going acoustician.
All this stuff means it tells me a lot about the energy arrival’s in the space and reflections and the tonality of the reverb and all that stuff.
And so there’s a lot of stuff that that when you and I walk into a space and we’ve got to mix to show we’re just like MAPP, you know, we this doesn’t really do anything for us because we can’t do anything about it. We’ve got to deal with it. We’ve got the best we can do is not hit the walls with our speakers. Right.
But if your job is to work on how sound decays in a space, if that is your living, this stuff is really important to you. Right. So so we’ve really been able to do a good job of characterizing that stuff. Now, I am going to go to two pane view here and you’ve got linnear.
You can actually do some really cool stuff here. We can we can actually render a spectrograph of this stuff and you can look at histograms so you can see 60 in octave bands or third octave bands. And there’s all kinds of cool stuff you can do in this mode. But I’m going to go to frequency. And so we’re we’re seeing basically the magnitude response of that data. And I’m going to apply a little smoothing just to get rid of the of the grass here.
OK, so what we can see is the Pinki, which was really, really short. He had a pretty lousy signal to noise ratio.
You’ll definitely see some wacky stuff happening in the low frequencies down here where my speaker is not putting out a ton of energy.
It’s a little speaker. So so the signal noise ratio down there is not great and we see that. But if you look at the overall trend here, these are all telling us pretty much the same story.
You can see that if our goal is I want to know the frequency response to this loud speaker, we pretty much got that. And what I’ll do is if you just sort of take note, hold this image in your mind and I’ll send you a graphic of this so you can kind of do a side by side in the video. If you want to make it easier for people to see.
I’m going to go back to real time mode. And I’m going to pull that measurement back up, and you see, again, if I put the same smoothing on, we have the same story, right?
There was a different. OK, so it’s a different vertical scale in our mode. But, you know, what you can see is this measurement happened instantly and gave us the same answer. Now, how do we know there’s noise, that there’s not noise in this matter? Well, that’s what the coherent trace does.
So you can see down here where we have the wacky behavior in in the the impulse or field measurements, we have low coherence. And that’s telling us the same thing. Hey, you know, there’s some some funky stuff going on here with your data. There’s noise. We’re not it’s not super high quality data. So, you know, I think we return to the first thing that you said, which is what am I trying to learn if I just need to measure system so I can IQ it?
I have no problem using a real time measurement for that, because it’s given me the answer that I need and I don’t have to wait. And like you said, I can tweak my IQ in real time or whatever, and I can just watch that. And I don’t have to go through that process again where I go take a bunch of sweeps.
So so, you know, going back to to this stuff, if if your interest is characterizing the decay of a of an environment, then yeah. Then it then it definitely makes sense to get into these specialty types of impulse response measurements or we can use specific signals and we can really dig down into that noise floor.
And that’s a real benefit, you know. But, you know, they’re there. I wouldn’t say that there’s one type of measurement that’s inherently superior. I think that they’re they’re different tools that are most effective at answering different types of questions.
Awesome, yeah, that’s that’s a lot more clear to me now after seeing that demo. Thanks, Michael. Yeah, absolutely, man. He did ever tell you about the time that I borrowed my friend Mark’s diesel Volkswagen Beetle? No, I would love to hear the story, though.
It’s actually pretty sad, but I do things like this in my life all the time. So this is back when I was living in Texas for a little bit after I’d moved back from living in Slovakia. I didn’t have a car and I was living with my parents for a little bit. And so I needed to go somewhere. I think it was actually I had a gig and I borrowed my friend Mark’s car. And then I was driving back home and I was like, Oh, I’ll do the nice friend thing and I’ll fill it up with gas.
And I did. And then I got back home and then I was driving it back to his house from my house. And then it stopped in my driveway and it wouldn’t turn back on. I was doing all the things that he taught me, like, you turn the key and you wait for it to the light. Come on. And then you started up and I could not figure it out. And I sat there for a long time. And I think it took me like 30 minutes before I was like back at the gas station.
It says all over the car, diesel, diesel, diesel, diesel. I just never you know, I don’t drive a diesel. So I of course, I pulled up and fill it up with I’m going to guess. So if you ever wonder how much it cost to fix that mistake and it cost one hundred fifty dollars to get the tank drained and to then put diesel gas in there again. So recently I posted a video where I was demoing using ping sweep with the transfer function and I posted it.
I was like, cool, this will be helpful. And then Christine just called me from National because they said, hey, you can’t do that. And I was like, What do you mean you can’t do that? Look, you can do this. You just go to a transfer function and you set the signal and he’s like, but you shouldn’t do that because that’s not what it once. And I was like, what do you mean? So so, Michael, why can’t I why shouldn’t I use the pink sweep when I’m just trying to do a transfer function?
So in real time mode, you know, there’s a reason that we use pink noise even though our measurement system is independent. Like, you know, we always say, yeah, you can use the feed from the soundboard, you can use music that you like. The reason that we tend to use pink noise anyway is that pink noise has all the frequencies at once. And so the measurement is acquired really quickly. So you can you can take that real time transfer function measurement with your favorite song, if you like.
You’re just going to be waiting a little longer for those little gaps to fill in. Right.
So if you think about the fact that when we’ve got holes in our spectrum, you know, we’ve got to wait for the analyzer to be fed some information at that frequency before it can show us the result.
And so when you use a a sweep, you’re talking about a signal that only has one frequency in it at a given time.
So all of the analysers bins, except for one hour waiting around going, well, I didn’t get anything yet. Right. And then when you combine that with a modern dual channel analyzer, has the multi time window or something similar where those time records up at the top end of that frequency response, they’re really, really quick. So they may just not get anything entirely.
So if you go into again, I don’t recommend that you do this, but if you go into your transfer function options and you choose something other than multi time window, you choose a 60 50.
So we’re generating that whole frequency range from one foot size.
Then you will see some data populate there with with the pink sweep. Now, it’s still it’s still not a great way to acquire the measurement because, you know, you’re talking about we have we’re going to run run more averages if we want to stabilize our measurement. You’re still feeding in a signal that is mostly empty and so for all the reasons that the MAPP that the impulse response measurements really like that right there, they’re waiting. They’re going to get the whole thing and then they’re going to go and process it and give you the result we’re trying to do in real time.
We’re trying to go as fast as we can. And so when you’re feeding in a signal that is mostly nothing, it’s obviously not the best way to do that. So much like you have the diesel car, you just put the wrong type of gas in it. Right. So we’re talking about two different types of measurement engines here. And you just you know, they can’t drink each other’s fuel. It’s the same thing. But as we saw, you know, they’re both going to get you where you need to go.
If you just want to know the frequency response rate, it’s just about feeding the mass what it’s expecting. And likewise, if you go into Roomi, Q Ezzard and you feed it some pink noise, that’s not going to work too well either. Right. So it’s all about the analyzer being fed, what it’s designed to, to process.
Yeah, that’s a really good point because in Rumiko as if you’re playing the signal that’s designed to reject all of that other noise and then I guess the same thing in Smart you, it knows what signal to expect. That’s why we have the loop back. Yeah. So, so this is all making more sense to me. Michael, thank you for, for coming on to talk to me about pink noise versus science. Is there anything just kind of on this or what do you want to say to kind of wrap up?
I know there’s like a take.
We talked about a lot of things here, but what’s kind of the take away that you want people to have might take away would be that you should use the measurement platform and measurement approach that that works best for you.
I think things like this where you gain an understanding of why these people choose to use this method and why these people choose this method. You know, I’m not I can’t speak for any manufacturer in why they chose to go one way or the other. But when you know what these measurements can do, then you can decide, well, OK, for the stuff that I’m doing, I think this this is going to be a good choice for me. As we’ve seen, they’re both going to get you there.
It’s all about at the end of the day, we want to just get our answer. We’re going to do a good job. We want to get a call back. Right. We want to we want to keep our gig. So I would encourage people to choose a tool that they’re comfortable with and that they understand and that they can get good results with. And as long as you’re doing that, I think that’s the way to go.