Deep Learning Based Intelligent Voiceprint Recognition, Positioning, and Perception in Cable Monitoring

Abstract

This article explores the application of deep learning based intelligent voiceprint recognition for cable monitoring, positioning and perception. In response to the monitoring needs of external force damage in cable trenches, the study combines deep learning technology and voiceprint recognition methods, aiming to achieve high-precision cable condition monitoring and damage localization. Utilizing convolutional neural networks (CNN) in deep learning models to automatically extract higher-level voiceprint features. Input the cable vibration signal to be recognized into the trained deep learning model, and the model will output the damage type corresponding to the signal. By comparing the model output with the preset damage type labels, automatic identification of cable external force damage can be achieved. The experimental results show that the application of deep learning based intelligent voiceprint recognition and positioning perception in cable monitoring has significant potential in improving the safety protection ability of cable trenches, providing a new technological approach for the intelligent monitoring and maintenance of cable systems.

Keywords

• Cable monitoring
• deep learning
• localization perception
• voiceprint recognition