Acoustic Sensing

We don’t usually speculate on the true identity of the hackers behind these projects, but when [TN666]’s accoustic drone-detector crossed our desk with the name “Batear”, we couldn’t help but wonder– is that you, Bruce? On the other hand, with a BOM consisting entirely of one ESP32-S3 and an ICS-43434 I2S microphone, this isn’t exactly going to require the Wayne fortune to pull off. Indeed, [TN666] estimates a project cost of only 15 USD, which really democratizes drone detection.

It’s not a tuba– Imperial Japanese aircraft detector being demonstrated in 1932. Image Public Domain via rarehistoricalphotos.com

The key is what you might call ‘retrovation’– innovation by looking backwards. Most drone detection schema are looking to the ways we search for larger aircraft, and use RADAR. Before RADAR there were acoustic detectors, like the famous Japanese “war tubas” that went viral many years ago. RADAR modules aren’t cheap, but MEMS microphones are– and drones, especially quad-copters, aren’t exactly quiet. [TN666] thus made the choice to use acoustic detection in order to democratize drone detection.

Of course that’s not much good if the ESP32 is phoning home to some Azure or AWS server to get the acoustic data processed by some giant machine learning model. That would be the easy thing to do with an ESP32, but if you’re under drone attack or surveillance it’s not likely you want to rely on the cloud. There are always privacy concerns with using other people’s hardware, too. [TN666] again reached backwards to a more traditional algorithmic approach– specifically Goertzel filters to detect the acoustic frequencies used by drones. For analyzing specific frequency buckets, the Goertzel algorithm is as light as they come– which means everything can run local on the ESP32. They call that “edge computing” these days, but we just call it common sense.

The downside is that, since we’re just listening at specific frequencies, environmental noise can be an issue. Calibration for a given environment is suggested, as is a foam sock on the microphone to avoid false positives due to wind noise. It occurs to us the sort physical amplifier used in those ‘war tubas’ would both shelter the microphone from wind, as well as increase range and directionality.

[TN] does intend to explore machine learning models for this hardware as well; he seems to think that an ESP32-NN or small TensorFlow Lite model might outdo the Goertzel algorithm. He might be onto something, but we’re cheering for Goertzel on that one, simply on the basis that it’s a more elegant solution, one we’ve dived into before. It even works on the ATtiny85, which isn’t something you can say about even the lightest TensorFlow model.

Thanks to [TN] for the tip. Playboy billionaire or not, you can send your projects into the tips line to see them some bat-time on this bat-channel.

Helium is inert, which makes it useful in a lot of different industries. But helium’s colorless and odorless non-reactivity also means traditional gas sensing methods don’t work. Specialized detectors exist, but are expensive and fussy. Thankfully, researcher [Li Fan] and colleagues found a physics-based method of detecting helium that seems as elegant as it is simple.

The new sensor relies on a topological kagome structure, and doesn’t depend on any chemical reaction or process whatsoever. The cylinders in the structure are interconnected; air can flow in and speakers at the three corners inject sound.

Sound waves propagate through the air within the structure at a fixed rate, and as helium enters the sensor it changes how fast the sound waves travel. This measurable shift in vibration frequency indicates the concentration of helium. It’s stable, calibration-free, doesn’t care much about temperature, and resets quickly. Even better, the three corners act as separate sensors, making it directional. It’s even quite rugged. Just as a basket weaved in a kagome pattern is stable and resistant to damage or imperfections in the individual strips that make up the pattern, so too is this sensor only marginally affected by physical defects.

The sensor design has been tested and shown to work with helium, but could possibly be applied to other gases. More detail is available at ResearchGate, with some information about the math behind it all in a supplemental paper.