  The time and expense required to collect and label audio data has been a
prohibitive factor in the availability of domain specific audio datasets. As
the predictive specificity of a classifier depends on the specificity of the
labels it is trained on, it follows that finely-labelled datasets are crucial
for advances in machine learning. Aiming to stimulate progress in the field of
machine listening, this paper introduces AeroSonicDB (YPAD-0523), a dataset of
low-flying aircraft sounds for training acoustic detection and classification
systems. This paper describes the method of exploiting ADS-B radio
transmissions to passively collect and label audio samples. Provides a summary
of the collated dataset. Presents baseline results from three binary
classification models, then discusses the limitations of the current dataset
and its future potential. The dataset contains 625 aircraft recordings ranging
in event duration from 18 to 60 seconds, for a total of 8.87 hours of aircraft
audio. These 625 samples feature 301 unique aircraft, each of which are
supplied with 14 supplementary (non-acoustic) labels to describe the aircraft.
The dataset also contains 3.52 hours of ambient background audio ("silence"),
as a means to distinguish aircraft noise from other local environmental noises.
Additionally, 6 hours of urban soundscape recordings (with aircraft
annotations) are included as an ancillary method for evaluating model
performance, and to provide a testing ground for real-time applications.
