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Conference Paper Flying Drone Classification based on Visualization of Acoustic Signals with Deep Neural Networks
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Authors
Soonyong Song, Youngsung Son, Youngil Kim
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.546-548
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289516
Abstract
In this paper, we proposed acoustic signal visualization for flying drone classifications and provided performance benchmarks for backbone deep neural networks. To visualize acoustic signals, we transformed the signals to 3-channel images by spectrogram. We put the images into deep neural networks to train their weights by transfer learning. We evaluated our classifiers by accuracy, recall, precision, and F1 score as well as loss. In our experiments, we accomplished maximum 99% drone classification performance in terms of accuracy with our dataset.
KSP Keywords
3-channel, Acoustic signal, As loss, Classification Performance, Deep neural network(DNN), Performance benchmarks, Transfer learning