МЕТОД ВИЯВЛЕННЯ ОЗНАК ЦИФРОВОГО МОНТАЖУ В ФОНОГРАМАХ З ВИКОРИСТАННЯМ НЕЙРОННИХ МЕРЕЖ ГЛИБОКОГО НАВЧАННЯ

• Date: 2020
• Main Authors: Solovyov, V.I., Rybalskiy, O.V., Zhuravel, V.V.
• Format: Article
• Language: English
• Published: V.M. Glushkov Institute of Cybernetics of NAS of Ukraine 2020
• Subjects: великі дані; нейронна мережа глибокого навчання; бінарна класифікація; модель; монтаж; цифрова обробка фонограм; цифрова фонограма
• Online Access: https://jais.net.ua/index.php/files/article/view/452
• Journal Title: Problems of Control and Informatics

In the last decade the models on the neuron networks of the deep learning have been effectively used for the decision of many actual tasks, requiring treatment of large arrays of data. The important task of examination of materials and apparatus of the digital audio recording belongs to them — automatic exposure of tracks of digital treatment (tracks of the digital editing) of phonograms. Editing of phonograms is produced in the pauses of speech information. Therefore the search of tracks of digital treatment is reduced to finding the signs of such editing in the pauses fixed on phonograms, and a process it is necessary to attribute to the task of binary classification. Complication of construction of such system consists in that, firstly, the signs of such editing are extraordinarily small and, secondly, their selection from the signals of pauses by the known classic methods of treatment is very problematic. The basic requirement to the expert tool is ability to provide a selection and obvious demonstration of signs of editing. Thus an expert must be convinced in reliability of results of examination. Therefore generally acknowledged impossibility of establishing a connection both between signals on an input with the results got on the output of the applied model and processes, taking place in it is the major factor of influence on further development of the systems of judicial examination on neuron networks. Authors suppose that for some tasks of binary classification, in particular, tasks of exposure of the digital editing of phonograms, such possibility exists. A research aim is a method of extraction of features of the digital editing of phonograms, suiting examination, based on application of neuron network of the deep learning. The method of exposure of signs of editing is offered and considered in pauses between the signals of speech information with the use of neuron network of the deep learning. It is suggested to expose the pauses of speech with the signs of editing by binary classification in a network. The aftertreatment of design results allows to get their graphic interpretation, that provides a selection in the separate array of fragments of pauses with the high degree of probability of correct classification. This provides possibility of creation of CAS of exposure of signs of editing in digital phonograms.