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The advancement of deep learning in environmentalacoustics is hindered by a lack of high-fidelity, labelleddatasets. While aircraft noise is a major environmental concern, collecting annotated data at scaleremains a logistical hurdle; attended measurementslack volume and permanent monitors lack geographicdiversity. This paper presents an autonomous aircraft noise recording station that integrates a noisemonitoring terminal with a custom solar-power subsystem and ADS-B (Automatic Dependent Surveillance–Broadcast) receiver. This architecture enablesautomatic alignment of acoustic events with flighttelemetry, including aircraft type, location and altitude in order to reduce manual annotation effort. Thispaper details a hardware architecture, energy budgetanalysis for year-round autonomy, and field protocolsfor data integrity. The resulting methodology enablesthe repeatable creation of ’AI-ready’ acoustic datasets.