AP22684173 "Development of a highly efficient neural network method for detecting voice activity at a low signal-to-noise ratio"

Relevance

In the context of the active development of voice technologies and increased requirements for information security, especially at low signal levels, the creation of a highly efficient VAD system based on deep neural networks is becoming an extremely urgent task. This will significantly improve the accuracy of voice activity recognition in a noisy environment and ensure reliable biometric identification.

Purpose

Development of a highly efficient neural network method and training of deep neural networks for detecting voice activity at a low signal-to-noise ratio

Expected and achieved results

1) Experimental studies necessary to determine the number of training epochs; 2) Conducting experimental studies to select the most appropriate activation function; 3) Experimental studies to conduct a comparative analysis of parameters (training accuracy, validation accuracy, test accuracy) and MLP, CNN, RNN.

Research team members with their identifiers (Scopus Author ID, Researcher ID, ORCID, if available) and links to relevant profiles