Practical Study of Recurrent Neural Networks for Efficient Real-Time Drone Sound Detection: A Review
Utebayeva D. Ilipbayeva L. Matson E.T.
January 2023Multidisciplinary Digital Publishing Institute (MDPI)
Drones
2023#7Issue 1
The detection and classification of engine-based moving objects in restricted scenes from acoustic signals allow better Unmanned Aerial System (UAS)-specific intelligent systems and audio-based surveillance systems. Recurrent Neural Networks (RNNs) provide wide coverage in the field of acoustic analysis due to their effectiveness in widespread practical applications. In this work, we propose to study SimpleRNN, LSTM, BiLSTM, and GRU recurrent network models for real-time UAV sound recognition systems based on Mel-spectrogram using Kapre layers. The main goal of the work is to study the types of RNN networks in a practical sense for a reliable drone sound recognition system. According to the results of an experimental study, the GRU (Gated Recurrent Units) network model demonstrated a higher prediction ability than other RNN architectures for detecting differences and the state of objects from acoustic signals. That is, RNNs gave higher recognition than CNNs for loaded and unloaded audio states of various UAV models, while the GRU model showed about 98% accuracy for determining the UAV load states and 99% accuracy for background noise, which consisted of more other data.
bidirectional LSTM , BiLSTM , deep learning , drone detection , gated recurrent networks , GRU , Kapre method , loaded UAV , LSTM , Mel-spectrogram , real-time UAV detection , RNN , simpleRNN , sound detection , UAV sound , unloaded UAV
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Department of ET and ST, Satbayev University, Almaty, 050013, Kazakhstan
Department of RET, International IT University, Almaty, 050040, Kazakhstan
Department of CIT, Purdue University, West Lafayette, 47907-2021, IN, United States
Department of ET and ST
Department of RET
Department of CIT
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