That’s a cool idea. I’d start by mounting a shotgun mic on a tripod near a traffic intersection, maybe a binaural mic on a head set so you get that true 3‑D feel of the road. Use a 24‑bit field recorder at 48 kHz to keep all the high‑frequency detail. Then step around the corner to capture the streetlight hum with a close‑mic or a passive contact mic on the pole—those low‑frequency whines are easier to isolate that way. When you have the files, load them into a spectral editor like Sonic Visualizer or MATLAB. You can plot a time‑frequency spectrogram and tag the peaks that match the traffic cadence. Then overlay those timestamps onto a GIS map or even a simple spreadsheet that maps latitude, longitude, and sound level. If you want to really quantify it, run a Fourier transform on each segment to extract the fundamental frequency of the hum, and use that as a “city noise index.” Finally, export the data and create a visual waveform that looks like a city map—high‑volume streets in red, low‑volume corners in blue, with the traffic pulse overlaid as a rhythmic contour. That should give you a neat acoustic blueprint of the city’s heartbeat.

I’ll bump it to 96 kHz then, and make sure the clock is synced to avoid that 20 ms drift on the binaural mic. For the streetlight hum, I’ll run a 50 Hz high‑pass on the contact mic signal so the low‑end bleed is cut clean. Splitting the recording into 15‑second blocks that match the traffic cycle will give the Fourier slices a consistent beat to latch onto. That should make the city noise index line up with the actual rhythm of the streets. Ready to get the gear out?