Applying the majority voting rule in acoustic detection and classification of drones

Rigel Fernandes, José Antonio Apolinário Jr., António Ramos, José de Seixas

DOI: 10.14209/sbrt.2021.1570726624

Evento: XXXIX Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2021)

Keywords: UAV audio processing threat detection target classification

Abstract

This paper discusses an approach to target detection and classification based on acoustic signals collected using one single microphone. We divide the problem into two parts, namely feature extraction and target detection and classification. We use an optimization step based on human auditory uncertainty. We employ a majority voting rule for every set of feature vectors, i.e., an estimate is only performed if the majority agrees. We conducted experiments using a single channel of the AIRA-UAS dataset, a public database of raw drone noises collected with an array of microphones mounted on a drone. This dataset comprises many different kinematics, with different spectra. The features we used are based on the Mel-Frequency Cepstral Coefficients (MFCC) and the Short-Time Fourier Transform of raw signals. We used the k-Nearest Neighbors algorithm for classification and adopted the cross-validation strategy to evaluate the method. We observed that the use of MFCC results in less biased estimations, which favors the voting strategy. The detection in the proposed method reached a probability of false positive near 0%, even with a small set of votes, and a classification accuracy of 99.1%. These metrics satisfy the requirements of most civilian and military applications.

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