The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft
Blake Downward, Jon Nordby

TL;DR
This paper introduces AeroSonicDB, a comprehensive dataset of aircraft sounds collected via ADS-B signals, designed to advance acoustic detection and classification systems for low-flying aircraft.
Contribution
It presents a novel passive data collection method using ADS-B transmissions and provides a detailed, labeled dataset with baseline classification results for machine listening research.
Findings
Dataset contains 625 aircraft recordings totaling 8.87 hours.
Baseline models achieved initial classification performance.
The dataset includes ambient and urban soundscape recordings for comprehensive testing.
Abstract
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…
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Taxonomy
TopicsAerodynamics and Acoustics in Jet Flows · Speech and Audio Processing · Underwater Acoustics Research
