Обоснование принципиальной возможности применения нейронных сетей глубокого обучения для построения системы выявления следов цифровой обработки фонограмм / Соловьев В. И., Рыбальский О. В., Журавель В. В. (2020)
Ukrainian

English  Cybernetics and Systems Analysis   /     Issue (2020, 56 (2))

Solovyov V.I., Rybalskiy O.V., Zhuravel V.V.
Substantiating the fundamental fitness of deep learnng neuron networks for construction of a system of for detecting traces of digital treatment of phonograms

The authors use the model of a deep learning neuron network to substantiate and verify principal applicability of such network to create a highly effective examination tool for phonogram digital processing detection. An experiment was conducted on more than 100,000 experimental fragments of unprocessed and processed phonogram pauses obtained automatically. The obtained dependences showed that when the probability threshold for correct binary classification of pauses is more than 0.55, it is possible to construct a highly effective examination tool. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

Keywords: deep learning neural network, digital editing, digital phonogram, editing points, examination, phonogram digital processing, Deep neural networks, E-learning, Learning systems, Binary classification, Detection system, Learning neural networks, Neuron networks, Probability threshold, Deep learning


Cite:
Solovyov V.I., Rybalskiy O.V., Zhuravel V.V. (2020). Substantiating the fundamental fitness of deep learnng neuron networks for construction of a system of for detecting traces of digital treatment of phonograms. Cybernetics and Systems Analysis, 56 (2), 182–188. doi: https://doi.org/10.1007/s10559-020-00249-2 http://jnas.nbuv.gov.ua/article/UJRN-0001103883 [In Russian].


 

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