The guitar ‘Toing’ sound from the ’70s was epic, and for the first time listener it was enough to get a bunch of people hooked to the likes of Aerosmith. Reverb units were all the rage back then, and for his DSP class project, [nebk] creates a reverb filter using Matlab and ports it to C++.
Digital reverb was introduced around the 1960s by Manfred Schroeder and Ben Logan. The system consists of essentially all pass filters that simply add a delay element to the input signal and by clubbing a bunch together and then feeding them to a mixer. The output is then that echoing ‘toing’ that made the ’80s love the guitar so much. [Nebk]’s take on it enlists the help of the Raspberry Pi and C++ to implement the very same thing.
In his writeup, [nebk] goes through the explaining the essentials of a filter implementation in the digital domain and how the cascaded delay units accumulate the delay to become a better sounding system. He also goes on to add an FIR low pass filter to cut off the ringing which was consequent of adding a feedback loop. [nebk] uses Matlab’s filter generation tool for the LP filter which he includes the code for. After testing the design in Simulink, he moves to writing the whole thing in C++ complete with the filter classes that allows reading of audio files and then spitting out ‘reverbed’ audio files out.
The best thing about this project is the fact that [nebk] creates filter class templates for others to play with. It allows those who are playing/working with Matlab to transition to the C++ side with a learning curve that is not as steep as the Himalayas. The project has a lot to learn from and is great for beginners to get their feet wet. The code is available on [GitHub] for those who want to give it a shot and if you are just interested in audio effects on the cheap, be sure to check out the Ikea Reverb Plate that is big and looks awesome.
Field recorders, or backpackable audio recorders with a few XLR jacks and an SD card slot, are a niche device, and no matter what commercial field recorder you choose you’ll always compromise on what features you want versus what features you’ll get. [Ben Biles] didn’t feel like compromising so he built his own multichannel audio DSP field recorder. It has a four channel balanced master outputs, with two stereo headphone outputs, eight or more inputs, digital I/O, and enough routing for multitrack recording.
Mechanically, the design of the system is a 3D printed box studded on every side with various connectors and patch points. This is what you get when you want a lot of I/O, and yep, those are panel mount connectors so get ready to pony up on the price of your connectors. The analog front end is a backplane sort of thing on a piece of perfboard, containing an eight channel differential I/O.
Of course any audio recorder is awful to use unless there’s a great user interface, and for that you can’t get any better than a high-resolution touchscreen on a phone. This led [Ben] to use Bluetooth to connect to an app showing the gain, levels, a toggle for phantom power, and a checkbox for line or microphone. If that’s not enough there are also some MIDI knobs for volume, because MIDI is still great for user input. It’s everything you want in a portable recording rig, and yes, there is a soundcloud demo. You can also check out a demo video below.
Effects pedals: for some an object of overwhelming addiction, but for many, an opportunity to hack. Anyone who plays guitar (or buys presents for someone who does) knows of the infinite choice of pedals available. There are so many pedals because nailing the tone you hear in your head is an addictive quest, an itch that must be scratched. Rising to meet this challenge are a generation of programmable pedals that can tweak effects in clever ways.
With this in mind, [ElectroSmash] are back at it with another open source offering: the pedalSHIELD MEGA. Aimed at musicians and hackers who want to learn more about audio, DSP and programming, this is an open-hardware/open-software shield for the Arduino MEGA which transforms it into an effects pedal.
The hardware consists of an analog input stage which amplifies and filters the incoming signal before passing it to the Arduino, as well as an output stage which does the DAC-ing from the Arduino’s PWM outputs, and some more filtering/amplifying. Two 8-bit PWM outputs are used simultaneously to make pseudo 16-bit resolution — a technique you can read more about in their handy forum guide.
The list of effects currently implemented covers all the basics you’d expect, and provides a good starting point for writing custom effects. Perhaps a library for some of the commonly used config/operations would be useful? Naturally, there are some computational constraints when using an Arduino for DSP, though it’s up to you whether this is a frustrating fact, or an opportunity to write some nicely optimised code.
One way to run a compute-intensive neural network on a hack has been to put a decent laptop onboard. But wouldn’t it be great if you could go smaller and cheaper by using a phone instead? If your neural network was written using Google’s TensorFlow framework then you’ve had the option of using TensorFlow Mobile, but it doesn’t use any of the phone’s accelerated hardware, and so it might not have been fast enough.
Google has just released a new solution, the developer preview of TensofFlow Lite for iOS and Android and announced plans to support Raspberry Pi 3. On Android, the bottom layer is the Android Neural Networks API which makes use of the phone’s DSP, GPU and/or any other specialized hardware to speed up computations. Failing that, it falls back on the CPU.
Currently, fewer operators are supported than with TensforFlor Mobile, but more will be added. (Most of what you do in TensorFlow is done through operators, or ops. See our introduction to TensorFlow article if you need a refresher on how TensorFlow works.) The Lite version is intended to be the successor to Mobile. As with Mobile, you’d only do inference on the device. That means you’d train the neural network elsewhere, perhaps on a GPU-rich desktop or on a GPU farm over the network, and then make use of the trained network on your device.
What are we envisioning here? How about replacing the MacBook Pro on the self-driving RC cars we’ve talked about with a much smaller, lighter and less power-hungry Android phone? The phone even has a camera and an IMU built-in, though you’d need a way to talk to the rest of the hardware in lieu of GPIO.
You can try out TensorFlow Lite fairly easily by going to their GitHub and downloading a pre-built binary. We suspect that’s what was done to produce the first of the demonstration videos below.
Software Defined Radio (SDR)–the ability to process radio signals using software instead of electronics–is undeniably fascinating. However, there is a big gap from being able to use off-the-shelf SDR software and writing your own. After all, SDRs require lots of digital signal processing (DSP) at high speeds.
Not many people could build a modern PC from scratch, but nearly anyone can get a motherboard, some I/O cards, a power supply, and a case and put together a custom system. That’s the idea behind GNU Radio and SDR. GNU Radio provides a wealth of Python functions that you can use to create sophisticated SDR application (or, indeed, any DSP application).
If Python is still not up your alley (or even if it is), there’s an even easier way to use GNU Radio: The GNU Radio Companion (GRC). This is a mostly graphical approach, allowing you to thread together modules graphically and build simple GUIs to control you new radio.
Even though you usually think of GRC as being about radios, it is actually a good framework for building any kind of DSP application, and that’s what I’ll show you in the video below. GRC has a signal generator block and interfaces to your sound card. It even has the ability to read and write data to the file system, so you can use it to do many DSP applications or simulations with no additional hardware.
If you’re just getting into software-defined radio (SDR) but you find some of the math and/or terminology a bit of hurdle, you could absolutely do worse than to check out these SDR tutorials by [Michael Ossmann]. While they’re aimed at people using his HackRF One tool (which we love), most of the tutorial videos are very generally applicable, and we realized that we hadn’t mentioned them explicitly before. Shame on us!
Ossmann focuses on SDR using the open-source GNURadio Companion GUI tool, which makes implementing a lot of cool SDR techniques as easy as dragging and dropping items into a flow diagram. If you want an overview of GNURadio or SDR in general, these videos are a must-watch.
In particular, we loved his entries on complex numbers and complex numbers in DSP because he goes through the whole rationale behind using imaginary numbers in radio work with a graphical presentation that helps add rationale to the otherwise slightly spooky math. Heck, watch these two even if you’re not interested in radio.
The newest entry, covering DSP filters includes a great hands-on introduction to finite impulse response (moving average) digital filters. We really like the practical, simulation-based approach presented in the video — it’s just perfect for a quick introduction.
So if you’re looking for a relatively painless way to get into SDR, grab yourself an RTL-SDR dongle, burn yourself a GNURadio Live DVD, and work through these videos.
Although I see a lot of wireless projects, I’m always surprised at the lack of diversity in the radio portions of them. I’m a ham radio operator (WD5GNR; I was licensed in 1977) and hams use a variety of radio techniques. If you think hams just use Morse code and voice communications, you are thinking of your grandfather’s ham radio. Modern hams have gone digital and communicate via satellites, video, and many different digital techniques that could easily have applicability to different wireless projects.
Of course, Morse code may have been one of the first digital modes. But hams have used teletype, FAX, and other digital modes for years. Now with PCs and soundcards in common use, hams have been on the forefront of devising sophisticated digital radio techniques.