Universal remotes are a handy tool to have around if you have many devices that would all otherwise have their own remote controls. Merging them all into a single device leads to less clutter and less frustration, but they are often not truly “universal” as some of them may not support every infrared device that has ever been built. If you’re in a situation like that it’s possible to build a truly universal remote instead, provided you have a microcontroller and a few infrared LEDs on hand.
This was the situation that [Matt] found himself in when his Amazon Fire TV equipment control feature didn’t support his model of speakers. To get around this he programmed an Arduino to essentially translate the IR codes from the remote and output a compatible set of codes to the speakers.This requires both an IR photodiode and an IR LED but little else other than the codes for the remote and the equipment in question. With that all set up and programmed into the Aruino, [Matt]’s remote is one step closer to being truly “universal”.
While [Matt] was able to make use of existing codes in the Arduino library, it is also possible to capture the codes required manually by pointing a remote at a photodiode and programming a microcontroller to capture the codes that you need. [Matt] used a Raspberry Pi to do this when debugging this project, but we’ve also seen this method used with a similar build which uses an ESP8266 to control an air conditioner via its infrared remote control capabilities.
The inspiration for it came from the human ability to hear music played by any instrument and to then be able to whistle or hum it, thereby translating it from one instrument to another. This is something computers have had trouble doing well, until now. The researchers fed their translator a string quartet playing Haydn and had it translate the music to a chorus and orchestra singing and playing in the style of Bach. They’ve even fed it someone whistling the theme from Indiana Jones and had it translate the tune to a symphony in the style of Mozart.
Shown here is the architecture of their network. Note that all the different music is fed into the same encoder network but each instrument which that music can be translated into has its own decoder network. It was implemented in PyTorch and trained using eight Tesla V100 GPUs over a total of six days. Efforts were made during training to ensure that the encoder extracted high-level semantic features from the music fed into it rather than just memorizing the music. More details can be found in their paper.
So if you want to hear how an electric guitar played in the style of Metallica might have been translated to the piano by Beethoven then listen to the samples in the video below.
A team of students in Antwerp, Belgium are responsible for Project Aslan, which is exploring the feasibility of using 3D printed robotic arms for assisting with and translating sign language. The idea came from the fact that sign language translators are few and far between, and it’s a task that robots may be able to help with. In addition to translation, robots may be able to assist with teaching sign language as well.
The project set out to use 3D printing and other technology to explore whether low-cost robotic signing could be of any use. So far the team has an arm that can convert text into finger spelling and counting. It’s an interesting use for a robotic arm; signing is an application for which range of motion is important, but there is no real need to carry or move any payloads whatsoever.
A single articulated hand is a good proof of concept, and these early results show some promise and potential but there is still a long ways to go. Sign language involves more than just hands. It is performed using both hands, arms and shoulders, and incorporates motions and facial expressions. Also, the majority of sign language is not finger spelling (reserved primarily for proper names or specific nouns) but a robot hand that is able to finger spell is an important first step to everything else.
Future directions for the project include adding a second arm, adding expressiveness, and exploring the use of cameras for the teaching of new signs. The ability to teach different signs is important, because any project that aims to act as a translator or facilitator needs the ability to learn and update. There is a lot of diversity in sign languages across the world. For people unfamiliar with signing, it may come as a surprise that — for example — not only is American Sign Language (ASL) related to French sign language, but both are entirely different from British Sign Language (BSL). A video of the project is embedded below.
We’re still about 150 years away from the invention of the universal translator by [Lt Cdr Sato] of the Enterprise NX-01, but [Dave] has something that’s almost as good: a speech recognition, translation, and text to speech setup for the Raspberry Pi that theoretically allows anyone to speak in sixty different languages.
After setting up all the Linux audio cruft, [Dave] digs in and starts on converting the guttural vocalizations of a meat speaker into something Google’s speech to text service can understand. From there, it’s off to Google again, this time converting text in one language into the writings of another.
[Dave]’s end result is a shell script that works reasonably well for something that won’t be invented for another 150 years. The video below shows the script successfully translating English to spanish, but it should work equally well with other languages such as dutch and latin, as well as less popular language such as esperanto and french.