Real Spectrum Analysis Goes Virtual

One of the hard things about electronics is that you can’t really see the working parts without some sort of tool. If you work on car engines, fashion swords, or sculpt clay, you can see with your unaided eye what’s going on. Electronic components are just abstract pieces and the real action requires a meter or oscilloscope to understand. Maybe that’s what [José] was thinking of when he built a-radio. This “humble experiment” pipes a scan from a software-defined radio into VR goggles, which can be as simple as a smartphone and some cardboard glasses.

The resulting image shows you what the radio spectrum looks like. Granted, so will a spectrum analyzer, but perhaps the immersion will provide a different kind of insight into radio frequency analysis.

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Illuminated 3D Printed Guitar Is Ready To Rock

When we think of 3D printed parts for our projects, most of us imagine little bits like brackets and mounting plates. Perhaps the occasional printed project enclosure. But if you’ve got a big custom printer as [Joshendy] does, plus plenty of time, it opens up a whole new world of large scale projects. Take for example the gorgeous RGB LED guitar body he recently completed.

Despite the considerable 300 x 300 mm build area of his custom 3D printer, [Joshendy] still had to design the guitar body in sections that could be bolted together after being printed in ABS. It took around 60 hours to run off all the parts, with the large central section taking the longest to print at 28 hours. With the generous application of heat-set inserts, the assembled guitar should be plenty strong.

The white ABS of the guitar body helps diffuse the LEDs.

While the skeletal plastic body of the guitar is certainly visually interesting in itself, it only makes up for half of the final look. Inside the central cavity, [Joshendy] has embedded two strips of RGB LEDs, a 128×64 OLED screen, and a custom PCB that plays host to a STM32L4 microcontroller the appropriate voltage regulators necessary to run it all on a battery pack.

The board taps into the audio being produced by the guitar and uses a fast Fourier transform (FFT) to get the LEDs reacting to the beat. As demonstrated in the video after the break, you can use the screen to navigate through the different lighting modes in real-time right on the instrument itself.

We covered the equally impressive large-format 3D printer that [Joshendy] used to produce this guitar earlier in the month, and it’s quite exciting to see the sort of things he’s printing on it already. This project has already set the bar very high, and we can’t wait to see what he comes up with next.

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DSP Spreadsheet: The Goertzel Algorithm Is Fourier’s Simpler Cousin

You probably have at least a nodding familiarity with the Fourier transform, a mathematical process for transforming a time-domain signal into a frequency domain signal. In particular, for computers, we don’t really have a nice equation so we use the discrete version of the transform which takes a series of measurements at regular intervals. If you need to understand the entire frequency spectrum of a signal or you want to filter portions of the signal, this is definitely the tool for the job. However, sometimes it is more than you need.

For example, consider tuning a guitar string. You only need to know if one frequency is present or if it isn’t. If you are decoding TouchTones, you only need to know if two of eight frequencies are present. You don’t care about anything else.

A Fourier transform can do either of those jobs. But if you go that route you are going to do a lot of math to compute things you don’t care about just so you can pick out the one or two pieces you do care about. That’s the idea behind the Goertzel. It is essentially a fast Fourier transform algorithm stripped down to compute just one frequency band of interest.  The math is much easier and you can usually implement it faster and smaller than a full transform, even on small CPUs.

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Raspberry Pi Takes Control Of Ham Radio

Today’s ham radio gear often has a facility for remote control, but they most often talk to a computer, not the operator. Hambone, on the other hand, acts like a ham radio robot, decoding TouchTone digits and taking action — for example, keying the radio and reading off the weather — in response to the commands received.

The code is in Python and uses numpy’s fast Fourier transform to identify digits. We’d be interested to test the performance of that compared to doing a Goertzel to specifically probe for the 8 digit tones: there are four row tones and four column tones. On the other hand, the FFT is handy and clearly works fast enough for this application.

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Name That Unknown RF Signal With A Little FFT Magic

Time was once that the amateur radio bands were an aurally predictable place. Spinning the dial up and down the bands, one heard familiar sounds – the staccato of Morse, the [Donald Duck] of sideband voice transmissions, and the occasional flute-like warble of radioteletype signals. Now, the ham bands are full of exotic signals encoding all manner of digital signals, each one with a unique sound and unique demodulation needs. What’s a ham to do?

Help is on the way. [José Carlos Rueda] has made progress toward automatically classifying unknown signals by modifying a Shazam-like app. Shazam is a popular smartphone app that listens to a few seconds of a song, creates an audio fingerprint of it, and searches a massive database of songs for a match. [Rueda] used a homebrew version of the app to search a SQL-lite database of audio fingerprints populated not with a playlist of popular music, but with samples from every known signal type in the Signal Identification Wiki. The database contains hashes for an FFT of each sample, which can be easily searched. With a five to ten second sample of a signal, captured either live over a microphone or from a recording,  he is able to identify the signal automatically.

Whether it be the weird, dissonant wail of PSK-31 or the angry buzzing of PACTOR, the goings-on across the bands no longer have to remain a mystery. We really like the idea here, and wonder if it can be expanded upon to visually decode signals based on their waterfall signatures using TensorFlow. There are some waterfall examples in [Danie Conradie]’s excellent article on RF modulation that could get you started.


Analyzing CNC Tool Chatter With Audacity

When you’re operating a machine that’s powerful enough to tear a solid metal block to shards, it pays to be attentive to details. The angular momentum of the spindle of a modern CNC machine can be trouble if it gets unleashed the wrong way, which is why generations of machinists have developed an ear for the telltale sign of impending doom: chatter.

To help develop that ear, [Zachary Tong] did a spectral analysis of the sounds of his new CNC machine during its “first chip” outing. The benchtop machine is no slouch – an Avid Pro 2436 with a 3 hp S30C tool-changing spindle. But like any benchtop machine, it lacks the sheer mass needed to reduce vibration, and tool chatter can be a problem.

The analysis begins at about the 5:13 mark in the video below, where [Zach] fed the soundtrack of his video into Audacity. Switching from waveform to spectrogram mode, he was able to identify a strong signal at about 5,000 Hz, corresponding to the spindle coming up to speed. The white noise of the mist cooling system was clearly visible too, as were harmonic vibrations up and down the spectrum. Most interesting, though, was the slight dip in frequency during the cut, indicating loading on the spindle. [Zach] then analyzed the data from the cut in the frequency domain and found the expected spindle harmonics, as well the harmonics from the three flutes on the tool. Mixed in among these were spikes indicating chatter – nothing major, but still enough to measure.

Audacity has turned out to be an incredibly useful tool with a broad range of applications. Whether it be finding bats, dumping ROMs, detecting lightning strikes, or cloning remote controls, Audacity is often the hacker’s tool of choice.

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Additive, Multi-Voice Synth Preserves Sounds, Too

For his final project in [Bruce Land]’s microcontroller design class, [Mark] set out to make a decently-sized synth that sounds good. We think you’ll agree that he succeeded in spades. Don’t let those tiny buttons fool you, because it doesn’t sound like a toy.

Why does it sound so good? One of the reasons is that the instrument samples are made using additive synthesis, which essentially stacks harmonic overtones on top the fundamental frequency of each note. This allows synthesizers to better mimic the timbre of natural, acoustic sounds. For each note [Mark] plays, you’re hearing a blend of four frequencies constructed from lookup tables. These frequencies are shaped by an envelope function that improves the sound even further.

Between the sound and the features, this is quite an impressive synth. It can play polyphonically in piano, organ, or plucked string mode through a range of octaves. A PIC32 runs the synthesizer itself, and a pair of helper PIC32s can be used to record songs to be played over. So [Mark] could record point and counterpoint separately and play them back together, or use the helper PICs to fine-tune his three-part harmony. We’ve got this thing plugged in and waiting for you after the break.

If PICs aren’t what you normally choose, here’s an FPGA synth.

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