OTTO: A Pi Based Open Source Music Production Box

Want an open source portable synth workstation that won’t break the bank? Check out OTTO. [Topisani] started OTTO as a clone of the well-known Teenage Engineering OP-1. However, soon [Topisani] decided to branch away from simply cloning the OP-1 — instead, they’re taking a lot of inspiration from it in terms of form factor, but the UI will eventually be quite different.

On the hardware side, the heart of the OTTO is a Raspberry Pi 3. The all-important audio interface is a Fe-Pi Audio Z V2, though a USB interface can be used. The 48 switches and four rotary encoders are wrangled by a pair of Arduino pro micros which pass the data on to the Pi. Data is related to the user through a 320×200 LCD.

The software is being written from scratch in C++17. If you’re not a hardcore C++ developer, don’t worry. The synth engines, audio effects, and other DSP software is written in Faust, which is a bit easier to learn.

OTTO is actively being developed, with synth engines already running, a prototype in progress, and fleshed out guidelines for programming the UI. If you’re into creating music, this one is worth checking out, as is Zynthian, another Raspberry Pi based synth.

Cigar Box Synth is a Fun Time

It’s fair to say that the groovebox market has exploded. Store shelves are overflowing with the umpteenth releases from KORG’s Volca line and the latest Pocket Operators. These devices often feature a wide array of tones in an enticingly compact and attractive package, but is it possible to build something similar at home? As [lonesoulsurfer] relates, it certainly is.

The Cigar Box Synth is, well… a synth, built in a cigar box. Based upon a 555 & 556 timer, and a 4017 decade counter, it provides a wealth of beepy goodness all crammed into a neat wooden package. We dig the cigar box form factor, as it’s a readily available wooden box often finished in an attractive way, and readily reworkable for all kinds of projects.

Sound is controlled with three master potentiometers, and there are four separate potentiometers to set the note for each of the four steps in the sequence. While its melodic abilities are limited to just four notes, it’s certainly something fun to play with and can act as a great jumping off point for further electronic experimentation in this area.

It takes us back to our guide on building DIY logic-based synthesizers – read on!

Synthbike Rolls To The Beat

Modular synthesizers are some of the ultimate creative tools for the electronic musician. By experimenting with patch leads, knobs and switches, all manner of rhythmic madness can be conjured out of the æther. While they may overflow with creative potential, modular synths tend to fall down in portability. Typically built into studio racks and composed of many disparate modules, it’s not the sort of thing you can just take down the skate park for a jam session. If only there was a solution – enter the madness that is Synth Bike.

Synth Bike, here seen in the 2.0 revision, impresses from the get go, being built upon a sturdy Raleigh Chopper chassis. The way we see it, if you’re going to build a synth into a bicycle, why not do it with some style? From there, the build ratchets up in intensity. There’s a series of sequencer modules, most of which run individual Arduino Nanos. These get their clock from either a master source, an external jack, or from a magnetic sensor which picks up the rotation of the front wheel. Your pace dictates the tempo, so you’ll want to work those calves for extended raves at the park.

The features don’t stop there – there are drums courtesy of a SparkFun WAV Trigger, an arcade button keyboard, and a filter board running the venerable PT2399 digital delay chip. It’s all assembled on a series of panels with wires going everywhere, just like a true modular should be.

The best thing is, despite the perplexing controls and arcane interface, it actually puts out some hot tunes. It’s  not the first modular we’ve seen around these parts, either.

 

Bringing Guitar Synthesis To The Microcontroller

If you’re working with audio in an embedded environment, the best option for years now has been the Teensy 3 microcontroller board. This choice has mostly been due to its incredible power and audio libraries, but until now we really haven’t seen a stompbox-style interface that used the Teensy to its fullest extent. Now we have, in [Wolkstein]’s GitSynth, everything you could want in a synthesizer that processes the signals from an electric guitar.

The core of this build is a Teensy 3, and all the audio goodies that come with that. Also included is a USB MIDI and audio interface, smartly both attached to a panel-mount USB-B connector on the back of the stompbox. Other controls include a single mono in jack for guitars and synths, two mono out jacks for stereo-ish output, a bunch of footswitches for bypass, tap tempo, preset selection, a jack for an expression pedal, and some buttons to move around the LCD user interface.

While putting a powerful microcontroller in a stomp box for is a project we’ve seen many times, this project really shines with the MIDI GUI that’s built for a device with a real display and a mouse. [Wolkstein] built a PyQt-based app for this synth, and it’s a plethora of buttons and sliders that looks similar enough to a real synthesizer. There’s enough configurability here for anyone.

You can check out the demo video (in German, but auto-translate subtitles exist) below.

Thanks [Mynaru] for the tip!

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Learn What Did and Didn’t Work In this Prototyping Post-Mortem

[Tommy] is a one-man-shop making electronic musical things, but that’s not what this post is about. This post is about the outstanding prototyping post-mortem he wrote up about his attempt to turn his Four-Step Octaved Sequencer into a viable product. [Tommy] had originally made a hand-soldered one-off whose performance belied its simple innards, and decided to try to turn it into a product. Short version: he says that someday there will be some kind of sequencer product like it available from him, “[B]ut it won’t be this one. This one will go on my shelf as a reminder of how far I’ve come.”

The unit works, looks great, has a simple parts list, and the bill of materials is low in cost. So what’s the problem? What happened is that through prototyping, [Tommy] learned that his design will need many changes before it can be used to create a product, and he wrote up everything he learned during the process. Embedded below is a demo of the prototype that shows off how it works and what it can do, and it helps give context to the lessons [Tommy] shares.

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Water Level Sensors, Alexa in a Fish, and Modular Synths During World Create Day

On Saturday we saw a flood of interesting hacks come to life as more than 100 community organized meetups were held for World Create Day. Thank you to all of the organizers who made these events possible, and for everyone who decided to get together and hack.

Students Learning Hardware Design in Islamabad, Pakistan

The students at LearnOBots took on a slew of great projects during World Create Day like a smart medicine dispenser, electronics that control mains appliances, parking sensors, and a waste bin that encourages you to feed it. The group did a wonderful job of showing off their event by publishing several updates with pictures, stories, and video presentations from all the students. Nice work!


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Google Builds A Synthesizer With Neural Nets And Raspberry Pis.

AI is the new hotness! It’s 1965 or 1985 all over again! We’re in the AI Rennisance Mk. 2, and Google, in an attempt to showcase how AI can allow creators to be more… creative has released a synthesizer built around neural networks.

The NSynth Super is an experimental physical interface from Magenta, a research group within the Big G that explores how machine learning tools can create art and music in new ways. The NSynth Super does this by mashing together a Kaoss Pad, samples that sound like General MIDI patches, and a neural network.

Here’s how the NSynth works: The NSynth hardware accepts MIDI signals from a keyboard, DAW, or whatever. These MIDI commands are fed into an openFrameworks app that uses pre-compiled (with Machine Learning™!) samples from various instruments. This openFrameworks app combines and mixes these samples in relation to whatever the user inputs via the NSynth controller. If you’ve ever wanted to hear what the combination of a snare drum and a bassoon sounds like, this does it. Basically, you’re looking at a Kaoss pad controlling rompler that takes four samples and combines them, with the power of Neural Networks. The project comes with a set of pre-compiled and neural networked samples, but you can use this interface to mix your own samples, provided you have a beefy computer with an expensive GPU.

Not to undermine the work that went into this project, but thousands of synth heads will be disappointed by this project. The creation of new audio samples requires training with a GPU; the hardest and most computationally expensive part of neural networks is the training, not the performance. Without a nice graphics card, you’re limited to whatever samples Google has provided here.

Since this is Open Source, all the files are available, and it’s a project that uses a Raspberry Pi with a laser-cut enclosure, there is a huge demand for this machine learning Kaoss pad. The good news is that there’s a group buy on Hackaday.io, and there’s already a seller on Tindie should you want a bare PCB. You can, of course, roll your own, and the Digikey cart for all the SMD parts comes to about $40 USD. This doesn’t include the OLED ($2 from China), the Raspberry Pi, or the laser cut enclosure, but it’s a start. Of course, for those of you who haven’t passed the 0805 SMD solder test, it looks like a few people will be selling assembled versions (less Pi) for $50-$60.

Is it cool? Yes, but a basement-bound producer that wants to add this to a track will quickly learn that training machine learning algorithms cost far more than playing with machine algorithms. The hardware is neat, but brace yourself for disappointment. Just like AI suffered in the late 60s and the late 80s. We’re in the AI Renaissance Mk. 2, after all.

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