Methods for Improving the Efficiency of Diagnostic Systems in the Neural Network-based Sound Analysis

Abstract

The study of the possibilities of modelling the use of neural networks while increasing the efficiency of diagnostic systems consists of creating a standard that would satisfy the conditions for maintaining the sound quality. At the same time, the effectiveness of diagnostic systems can be considered when applied both in a technological environment and in a virtual space. The relevance of the study is determined by the possibilities of using the reference sound, which forms and uses the basis of the neural networks. The scientific novelty of the study is determined by the fact that an adaptive method for creating standards of units of measurable quantities with specified accuracy characteristics is proposed, subject to limited resources. The first version of the mathematical model of the measurement procedure is formed during reproduction, storage and transmission of a unit of measurement developed on the basis of a physical model, which, in turn, is built in accordance with a priori information on the principle of reproduction (storage and transmission) of a unit of measurement, a list of informative parameters and influential quantities when measuring. The authors have developed the necessary accuracy characteristics, specified by the technical specifications and determined the resources allocated for the creation of the standard. The practical significance of the study lies in the establishment of distributed networks for sound quality measurement, mainly within the structures of the study of sound transmission between high-tech devices.