We are truly living in the golden age of media streaming. From the Roku to the Chromecast, there is no shortage of cheap devices to fling your audio and video anywhere you please. Some services and devices may try to get you locked in a bit more than we’d like (Amazon, we’re looking at you), but on the whole if you’ve got media files on your network that you want to enjoy throughout the whole house, there’s a product out there to get it done.
It should come as no surprise that a military ammo can has quite a bit more space inside than is strictly required for the Raspberry Pi 3 [Zwaffel] based his project on. But it does make for a very comfortable wiring arrangement, and offers plenty of breathing room for the monstrous 60 watt power supply he has pumping into his HiFiBerry AMP+ and speakers.
On the software side the Pi is running Max2Play, a Linux distro designed specifically for streaming audio and video remotely. [Zwaffel] says that with this setup he is able to listen to music on his Squeezebox server as well as watch movies via Kodi.
There are quick hacks, there are weekend projects and then there are years long journeys towards completion. [Boris Vitazek]’s grafofon falls into the latter category. His creation can best be described as electromechanical sequencer synthesizer with a multiplayer mode.
The storage medium and interface for this sequencer is a thirteen-meter loop of paper that is mounted like a conveyor belt. Music is composed by drawing on the paper or placing objects on it. This is usually done by the audience and the fact that the marker isn’t erased make the result collaborative and incremental.
These ‘scores’ are read by a camera and interpreted by software.This is a very vague description of this device, for a reason: the build went on over six years and both hard- and software went through several revisions in that time. It started as a trigger for MIDI notes and evolved from there.
In his write up [Boris] explains the technical aspects of each iteration. He also tells the stories of the people he met while working on the grafofon and how they influenced the build. If this look into the art world reminds you of your local hackerspace, it is because these worlds aren’t that far apart.
Echolocation projects typically rely on inexpensive distance sensors and the human brain to do most of the processing. The team creating SNAP: Augmented Echolocation are using much stronger computational power to translate robotic vision into a 3D soundscape.
The SNAP team starts with an Intel RealSense R200. The first part of the processing happens here because it outputs a depth map which takes the heavy lifting out of robotic vision. From here, an AAEON Up board, packaged with the RealSense, takes the depth map and associates sound with the objects in the field of view.
Binaural sound generation is a feat in itself and works on the principle that our brains process incoming sound from both ears to understand where a sound originates. Our eyes do the same thing. We are bilateral creatures so using two ears or two eyes to understand our environment is already part of the human operating system.
In the video after the break, we see a demonstration where the wearer doesn’t need to move his head to realize what is happening in front of him. Instead of a single distance reading, where the wearer must systematically scan the area, the wearer simply has to be pointed the right way.
Life is good if you are a couch potato music enthusiast. Bluetooth audio allows the playing of all your music from your smartphone, and apps to control your hi-fi give you complete control over your listening experience.
Not quite so for [Daniel Landau] though. His Cambridge Audio amplifier isn’t quite the latest generation, and he didn’t possess a handy way to turn it on and off without resorting to its infrared remote control. It has a proprietary interface of some kind, but nothing wireless to which he could talk from his mobile device.
His solution is fairly straightforward, which in itself says something about the technology available to us in the hardware world these days. He took a Raspberry Pi with the Home Assistant home automation package and the LIRC infrared subsystem installed, and had it drive an infrared LED within range of the amplifier’s receiver. Coupled with the Home Assistant app, he was then able to turn the amplifier on and off as desired. It’s a fairly simple use of the software in question, but this is the type of project upon which so much more can later be built.
Have a beautiful antique radio that’s beyond repair? This ESP8266 based Internet radio by [Edzelf] would be an excellent starting point to get it running again, as an alternative to a Raspberry-Pi based design. The basic premise is straightforward: an ESP8266 handles the connection to an Internet radio station of your choice, and a VS1053 codec module decodes the stream to produce an audio signal (which will require some form of amplification afterwards).
Besides the excellent documentation (PDF warning), where this firmware really shines is the sheer number of features that have been added. It includes a web interface that allows you to select an arbitrary station as well as cycle through presets, adjust volume, bass, and treble.
If you prefer physical controls, it supports buttons and dials. If you’re in the mood for something more Internet of Things, it can be controlled by the MQTT protocol as well. It even supports a color TFT screen by default, although this reduces the number of pins that can be used for button input.
The firmware also supports playing arbitrary .mp3 files hosted on a server. Given the low parts count and the wealth of options for controlling the device, we could see this device making its way into doorbells, practical jokes, and small museum exhibits.
There are few greater follies in the world of electronics than that of an electronic engineering student who has just discovered the world of hi-fi audio. I was once that electronic engineering student and here follows a tale of one of my follies. One that incidentally taught me a lot about my craft, and I am thankful to say at least did not cost me much money.
It must have been some time in the winter of 1991/92, and being immersed in student radio and sound-and-light I was party to an intense hi-fi arms race among the similarly afflicted. Some of my friends had rich parents or jobs on the side and could thus afford shiny amplifiers and the like, but I had neither of those and an elderly Mini to support. My only option therefore was to get creative and build my own. And since the ultimate object of audio desire a quarter century ago was a valve (tube) amp, that was what I decided to tackle.
Nowadays, building a valve amp is a surprisingly straightforward process, as there are many online suppliers who will sell you a kit of parts from the other side of the world. Transformer manufacturers produce readily available products for your HT supply and your audio output matching, so to a certain extent your choice of amp is simply a case of picking your preferred circuit and assembling it. Back then however the world of electronics had extricated itself from the world of valves a couple of decades earlier, so getting your hands on the components was something of a challenge. I cut out the power supply by using a scrap Dymar Electronics instrument enclosure which had built-in HT and heater rails ready to go, but the choice of transformers and high-voltage capacitors was something of a challenge.
Pulling the amplifier out of storage in 2017, I’m going in blind. I remember roughly what I did, but the details have been obscured by decades of other concerns. So in an odd meeting with my barely-adult self, it’s time to take a look at what I made. Where did I get it right, and just how badly did I get it wrong?
[Roland]’s workflow consists of breaking up a recording from his backyard into one second clips, loading them in to a Python program and running some machine learning code to determine whether the clip is a recording of a bat or not and using this to determine the number of bats flying around. He uses several Python libraries to do this including Tensorflow and LibROSA.
The Python code breaks each one second clip into twenty-two parts. For each part, he determines the max, min, mean, standard deviation, and max-min of the sample – if multiple parts of the signal have certain features (such as a high standard deviation), then the software has detected a bat call. Armed with this, [Roland] turned his head to the machine learning so that he could offload the work of detecting the bats. Again, he turned to Python and the Keras library.