Planet Arduino

What we like most about [GreatScott’s] project videos is that he not only shows making them but also the calculations for selecting parts and the modifications along the way. This time he’s made a mini spy bug that records up to nine hours of audio.

His first task was to figure out if the ATmega328p’s ADC is suitable for audio sampling, but only after he explains how sampling works by periodically checking the input voltage from the microphone. Checking the datasheet he found that the ADC’s fastest conversion time is 13 microseconds, which works out to a sampling rate of 76.923 kHz. Good enough.

He then walks through why and how he decided to go with a pre-made amplifier circuit built around the MAX9814 IC. Spoiler alert. His electret’s amplifier output voltage was too low, using an off-the-shelf circuit instead of making his own kept things simple, and the circuit has automatic gain control.

At this point, he added the MicroSD card adapter. Why not just transmit the audio over FM as so many others have done with their hacks? Perhaps he’s worried about someone detecting the transmission and finding his bug.

His final optimization involved getting a good battery life. He measured the circuit’s current draw at 20 milliamps. With a 160 mAh battery capacity, that would be 8 hours of recording time. Removing the Arduino Pro Mini’s voltage regulator and two LEDs got the current down to 18 milliamps and a recording time of 9 hours. Better.

Those are the highlights. Enjoy his full walkthrough in the video below.

At Arduino Day, I talked about a project I and my collaborators have been working on to bring machine learning to the maker community. Machine learning is a technique for teaching software to recognize patterns using data, e.g. for recognizing spam emails or recommending related products. Our ESP (Example-based Sensor Predictions) software recognizes patterns in real-time sensor data, like gestures made with an accelerometer or sounds recorded by a microphone. The machine learning algorithms that power this pattern recognition are specified in Arduino-like code, while the recording and tuning of example sensor data is done in an interactive graphical interface. We’re working on building up a library of code examples for different applications so that Arduino users can easily apply machine learning to a broad range of problems.

The project is a part of my research at the University of California, Berkeley and is being done in collaboration with Ben Zhang, Audrey Leung, and my advisor Björn Hartmann. We’re building on the Gesture Recognition Toolkit (GRT) and openFrameworks. The software is still rough (and Mac only for now) but we’d welcome your feedback. Installations instructions are on our GitHub project page. Please report issues on GitHub.

Our project is part of a broader wave of projects aimed at helping electronics hobbyists make more sophisticated use of sensors in their interactive projects. Also building on the GRT is ml-lib, a machine learning toolkit for Max and Pure Data. Another project in a similar vein is the Wekinator, which is featured in a free online course on machine learning for musicians and artists. Rebecca Fiebrink, the creator of Wekinator, recently participated in a panel on machine learning in the arts and taught a workshop (with Phoenix Perry) at Resonate ’16. For non-real time applications, many people use scikit-learn, a set of Python tools. There’s also a wide range of related research from the academic community, which we survey on our project wiki.

For a high-level overview, check out this visual introduction to machine learning. For a thorough introduction, there are courses on machine learning from coursera and from udacity, among others. If you’re interested in a more arts- and design-focused approach, check out alt-AI, happening in NYC next month.

If you’d like to start experimenting with machine learning and sensors, an excellent place to get started is the built-in accelerometer and gyroscope on the Arduino or Genuino 101. With our ESP system, you can use these sensors to detect gestures and incorporate them into your interactive projects!