 This week's INMPI is from ST, Lydia, what is this week's new product introduction? That's right. ST Micro. This is an interesting new sensor. This is an accelerometer, it's the AIS25BA, I want to also mention that this is the automotive version of the LIS25BA, so there's two versions that work pretty much the same. And these accelerometers, that you configure them with I2C, but the data comes out via TDM. And they're designed to be low noise, high bandwidth, they're triple access, and yeah, it's interesting. They basically go up to basically 4G max, and instead of getting data the way you normally would with an accelerometer where you basically query it over I2C or SPI and you get the data that way, or maybe it's analog, the data is streamed out for you automatically over a high speed TDM interface where you can clock it at multiple kilohertz, and it's designed to go into an audio subsystem, which is like, I was like, what's up with that? Why would such a thing be useful? This is the datasheet, so this one again, this is the AIS version, it's designed for use in automotive. It's got ultra-low noise density, it's a 1.8 volts interface, by the way, which isn't a big deal, but just FYI. And it's designed to have ultra-low frequency response from DC to 2.4 kilohertz, so 2.4 kilohertz is kind of like the cutoff where they're expecting you to stream data out of, you control it over I2C, and then of course you get, like I said, TDM output 8 to 24 kilohertz, it's time division, that's TDM sense for time division multiplex, you're going to get the X, Y, Z, and other data out. So again, what is this useful for? So this is kind of interesting, so the idea behind this is that you would use this for noise reduction or noise cancellation for recording, because usually you record with a microphone, and a microphone takes audio waves going through the air, and it flexes a thin piezo element or a MEMS element, converts it into a capacitive or current or voltage measurement, and that's how you get audio out. The problem is that, and just we're doing the show, and so this is something we deal with, it's really hard to get only the audio you want without the background noise you don't want, it's like there's a lot of work involved in it. And especially if you're doing automotive, right, we mentioned this is the automotive version. Cars are extremely loud, and you're trying to maybe use a voice assistant or you're talking on the phone, you're fighting all this background noise, and what's interesting about this idea that ST, I don't know if they invented it or they just have products for it, but you use the accelerometer to do the vibration detection for the low, the DC zero to again about two kilohertz vibrations, and you use that instead of the microphone, and so you don't end up getting the acoustic noise, and you cancel it out, and if there is noise that comes in from the low frequencies of the vibration, you can either add it or remove it or perform some sort of filtering so that you can, you don't get that, especially in those low frequencies is where you're getting a lot of acoustic noises, and microphones usually are not as responsive. So yeah, so the idea here is that you still need a microphone, this is not a microphone, but because it's TDM out, you can basically shove it directly into your Kodak, your DSP, your chip, your microcontroller, your microcomputer, perform some basic filtering on it, either like low pass or high pass, add or addition, and use that to get better audio output. And we have a video that will show at the end that actually it shows how nice the waveform looks when you remove the audible noise that you would not get through the vibration detection of the LIS or the AIS-25. So the chip is the standard 16 LIS style GPIO, but again, they don't have SPI, instead they have TDM output or input, you put in the bit clock and the M clock for the, sorry, the word clock and the M clock and the master clock and the B for the bit clock. You control over I squared, see what data you want out and the formatting. It comes out as 16 bit TDM, I think up to eight channels. I think maybe, yeah, here it shows you, you can decide, you know, which frequency range you want. You can set cutoffs, you can do over sampling, whatever. You can get the ODR data, X, Y, Z, it comes out as TDM, and then your your codec is going to have to take that and then do something with it. But usually once you get it, like if you have a powerful enough back controller on my computer, once you get the data in in this like I2S like format, it's very easy for you to perform audio calculations on it. I'm just mentioning that the accelerometer is just accelerometer, it doesn't do the math for you, but it just gets you the data in the format that you can then perform math on. So, yeah, you can, sorry, there's six, sorry, there's eight slots, I think. There's six slots. You can decide which ones you want on what axis. And then, you know, again, they have some example code for, I think STM microcontroller, you know, if you download it for their dev board. But honestly, I would probably plug this into something like a Raspberry Pi computer, something that can really do the analysis for you and do the filter cutoffs. For the iSquad C control side, you know, they do have a driver. This is kind of a generic C driver. It's not in Arduino Ease, but you can port it to, you know, whatever Linux, STM32, CUBE, or whatever microcontroller you're using and then pipe that TDM data into your microcontroller, sorry, into your microcomputer or your DSP. And the best part is it's in stock. Available at Digi-Key. Yay. That's right. It's in stock. There's 490 at the time of this, this printing. Yes, there's no eval boards right now. I would have picked up a val board and I'd try it out. But do check out also, again, the LIS25, which is the non-automotive version. I think the thing that we first of all, this is interesting because I'd never seen Acceleron with TDM output. And I was like, why would you do that? But then once I saw the demo and I read about it, I was like, oh, this makes a lot of sense. It's definitely, you know, as we're seeing, you know, we've talked a lot about AI and audio interfaces. You know, one thing that I've noticed is a lot of companies are trying to get away from having mechanical button and knob interfaces and go with audio interfaces because they're upgradable. They're programmable. They don't get dirty. They don't get loose. You don't have to worry about people looking. You know, you can just speak to your car and say, you know, do X, Y, Z. The problem is that you are if an audio assistant isn't 99 percent accurate, it's incredibly annoying. Like they have to be so good or it's very frustrating for people. And it's even not possible because there's humans involved. So I saw that the studies that have come out that it's a better UI for many things to have physical knobs. So I think it's like the middle path is the way, once again, like some stuff makes sense for probably voice control, but some stuff should probably also have a tactical knob. Probably can do a little bit of both. Yeah. That said, you know, this was definitely I can I can sort of tell like this is designed for some car company that they were like, we want audio interface, but we want to do the noise cancellation from the vibration because it's like you're driving on a highway and it's just like, er, so. But I think it's going to be very useful for other, you know, anyone using recording or voice, especially with. Or doing actually another interesting use case for this would be if you wanted to do like predictive maintenance type projects where it's like you have a device that's vibrating like a compressor, like this is a common thing, the compressor. It's like what starts it starts making a weird noise, right? But then how do you determine what that weird noise is and how do you get the audio sample? Using the vibration could be nice because then you won't you won't be affected that it won't be affected by outside acoustic noise. So there's a couple of cases I like the idea of like it streams in as audio comes in as three, you know, three to nine channels and you tweak it. That way the elevators they take any time it's about to break down. It always sounds different. Yeah, that would probably be a good news. It's interesting that, you know, it's we forget that accelerometers and audio are actually measuring the same thing. This one does a surface when one does it in the air. Anyways, we're going to distract. But check out this very cool demo, which I like that they had audacity so you can really see the effects of the LIS 25. Ordered by the accelerometer and the audio is recorded by the onboard microphone. In this PC GUI, you see the individual part of each of the sensors, which is the microphone on the top. You see in time and frequency and then the accelerometer in time and frequency. What we're going to do in order to deploy the system is we're going to take the lower portion of the spectrum up to two kilohertz from the accelerometer and the higher portion of the spectrum from the microphone, as you can see in these two diagrams. We are going to combine them into a single audio signal where the gain acoustic gains of the two sensors have been equalized and recombined them into the output that gets generated as output of the system. So now let's try to record the audio coming from the demo. So we turn on this recording system where we see two channels. Actually, one represents the audio as recorded by the single microphone. Another one represents the audio recorded by a combination of microphone and accelerometer. So let's try to listen to each one of these recording in order to understand the improvement in quality. I'm going to split the two signals into individual tracks so that we can listen back to them. Now, if I turn on this bottom track and I play it back from the loud speaker, you can hear a very disturbed audio that represents what you would get using just a microphone with no further processing. If we listen to the other track, we can hear the output of the actual system, which is clear voice with no of the noise you were hearing previously.