Selection of acoustic features in unmanned aerial vehicle detection tasks

With the increasing number of incidents involving the unauthorized use of unmanned aerial vehicles (UAVs), the development of effective methods for their automatic detection has become increasingly relevant. This article provides a concise overview of current approaches to UAV detection, with particular emphasis on acoustic monitoring methods, which offer several advantages over radio-frequency and visual systems. The main acoustic features used for recognizing drone sound signals are examined, along with techniques for extracting these features using open-source libraries such as Librosa and Essentia. To evaluate the effectiveness of various features, a balanced dataset was compiled and utilized, containing audio recordings of drones and background noise. A multi-stage feature selection methodology was tested using the Feature-engine library, including the removal of constant and duplicate features, correlation analysis, and feature importance assessment. As a result, a subset of 53 acoustic features was obtained, providing a balance between UAV detection accuracy and computational cost. The mathematical foundations of spectral feature extraction are described, including different types of spectrograms (mel-, bark-, and gammatone-spectrograms), as well as vector and scalar acoustic features. The results presented can be used to develop automatic UAV acoustic detection systems based on machine learning methods.

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