What is a good way to distinguish aircraft noise from other sounds? The FAA's NextGen routes, which were modeled, not measured, have created a bunch of aircraft noise for tens of thousands of people. I want to make it easy for those people (including me) to buy a cheap USB microphone, plug it into a laptop or PC, detect aircraft noise, correlate that with publicly available flight-path data, and file complaints with the FAA, airport(s), and legislators. With enough actual (as opposed to theoretical) data, we might be able to get them to roll back the route changes. Or not, but it's worth a try.

To do that, I need some help with the "detect aircraft noise" part of the problem. It's not enough to trigger off noise above a certain dB threshold, because lawnmowers, leaf blowers, trucks, etc. also make noise. I can weed out some over-threshold noise that doesn't last long enough or lasts too long (aircraft take 5-10 seconds to pass overhead, vs. minutes for lawnmowers and a couple of seconds for trucks), but that's a pretty crude approach. And I still need to be able to factor multiple noise sources into individual components.

Correlation would probably be easier with a pair or array of microphones, to establish noise direction that could be tied to flight paths, but that adds a lot more hardware and complexity. Ideally we'd just need one microphone.

Aircraft include airplanes (jets, turboprops, and standard propellor-driven), as well as helicopters.

I'm a good coder (Java expert, former C expert), but have zero experience with DSP or training algorithms. Can you help me get started?
You need to train a classifier. The way I would approach it is to allow users to submit audio recordings through your app, and indicate which sections of the recordings contain the plane. This would enable you to quickly crowd source a lot of data with which to build a model. Then you would find the STFT and volume of the signal over time. Finally, train the classifier (such as an SVM or neural network) using the amplitude spectrum and volume features. Once you have trained the classifier, you can use it to detect planes by extracting the same features on live audio (STFT isn't computationally expensive, so it should be feasible to do on the phone).

Here's a backgrounder on Audio signal classification.