Hackers everywhere are having a lot of fun with SDR – as is obvious from the amount of related posts here on Hackaday. And why not, the hardware is cheap and easily available. There are all kinds of software tools you can use to dig in and explore, such as SDR# , Audacity, HDSDR and so on. [illias] has been following SDR projects for a while, which piqued his interest enough for him to start playing with it. He didn’t have any real project in mind so he focused on studying the methodology and the tools available for analyzing 433MHz RF transmission. He describes the process of using MATLAB to recover the transmissions being received by the SDR
He started off by studying the existing tools available to uncover the details of the protocol. The test rig uses an Arduino UNO with the rc-switch library to transmit via a common and inexpensive 433MHz module. SDR# is used to record the transmissions and Audacity allows [illias] to visualize the resulting .wav files. But the really interesting part is where he documents the signal analysis using MATLAB.
He used the RTL-SDR package in conjunction with the Communications System Toolbox to perform spectrum analysis, noise filtering and envelope extraction. MATLAB may not be the easiest to work with, nor the cheapest, but its powerful features and the fact that it can easily read data coming from the SDR makes it an interesting tool. For the full skinny on what this SDR thing is all about, check out Why you should care about Software Defined Radio.
[Nick]’s grandfather was quite the old school hacker. In the 1940s, he built his own wire recorder and microphone to capture everything from his children’s Chirstmas wishes to his favorite songs and programs from the radio. Only 20 or so spools have survived and were doomed to silence until [Nick] was able to find a vintage wire recorder, restore it, and feed digitized audio into Audacity.
Once he restored one of the two machines that he was able to get his hands on, [Nick] was in business. Since his grandfather also rolled his own spools, [Nick] had to build a playback spindle that would hold them. His uncle found an old mechanical counter to do the job, which [Nick] secured to the workbench. He fed the output from the wire recorder’s playback head into a guitar pre-amp, effectively digitizing the audio for recording in Audacity.
After playing all the spools, he adjusted the levels where necessary and cleaned up the recordings. His biggest challenge was feeding the wires back on to their original spools, which he managed with an electric drill and a rubber grommet. Be sure to check out the mp3 clips on [Nick]’s page. If you’re in the mood for old audio hacking stories, here’s one about building a tape recorder in 1949.
[Noah Farrington] has just accomplished a major milestone in his life, purchasing his first car! A glorious 2001 Ford Focus wagon. While it may be a fully loaded luxury vehicle, it is missing one thing poor [Noah] can’t live without. An aux-in port.
He had a few options for rectifying the situation. Live with it as is, hack the strange Ford media protocol out of the back, or fool the CD player into playing his input. Naturally he chose the third option.
His first challenge was removing the deck from the car. People told him he’d have to buy fancy stereo removal tools — he made do with tent pegs and coat hangers. Using the same method as described in a past aux-in hack, he identified the audio in leads on the CD player’s ribbon cable. By carefully soldering in his own aux-in plug, he’s almost ready for business! Unfortunately, the CD player also needs to think that it is on for it to properly output the audio. [Noah] chose the simple solution — record a silent CD to always leave in the deck.
Stick around after the break to see it in action.
Continue reading “Un-crapifying a Car Stereo”
[Jason] just tipped us off about his recent experiment, in which he creates a sonar system using standard audio equipment and a custom Python program. In case some of our readers don’t already know it, Sonar is a technique that uses sound propagation to detect objects on or under the surface of the water. It is commonly used in submarines and boats for navigation. [Jason]’s project uses active sonar, which consists in sending short audio bursts (chirps) and listening for echoes. The longer it takes for the echo to return, the further the object is. Though his proof of concept is not used underwater, that may change if he continues the project.
The audio editing software Audacity was used to make a fast frequency changing chirp, along with PyAudio libraries for the main Python program. Exact time of arrival is detected by correlating the microphone output with the transmitted signal. Given that [Jason] uses audible frequencies, we think that the final result shown in the video embedded below is quite nice.
Continue reading “Sonar With Python and Conference Call Hardware”
[Jaroslav’s] camera didn’t have a feature to measure the speed of its response in different modes so he figured out his own method. Using the microphone on his webcam he recorded the sound made by the mirror and shutter movements, then used Audacity to analyze the camera’s performance.
When you get right down to it, this is a fantastic idea. Audacity, the open source audio editing suite, has the ability to show each captured audio track next to each other. That makes it easy for you to precisely align the clips, and has in-build time measuring features with fantastic resolution.
He tested a whole bunch of different settings on a Canon EOS600D DSLR camera. In the image above you can see him comparing performance between different ISO settings. He also looks into different brands and sizes of SD storage cards, as well as the time difference when storing raw image data versus JPEG encoded data.
[Charlie X-Ray] is having some modern fun with the phone system by pulling dialed numbers from the audio track of YouTube videos (translated). The first step was to find a video where a telephone is being dialed and the sounds of the keypresses are audible. You can’t tell those tones apart, but a computer can. That’s because each number pressed generates a combination of two out of seven closely related frequencies. [Charlie] isolated the audio using Audacity, then wrote a python script to generate a spectrogram like the one above. By matching up the two dark nodes you can establish which two frequencies were played and decode the phone number being dialed. So how does this work again… find audio of a phone being dialed, decode the number.. profit?