Authentication of musical speech devices based on RF fingerprint recognition

Music-voice devices are becoming more and more abundant and diverse with the development of technology; however, their security and privacy issues have also attracted widespread attention. Therefore, the research aims to explore the authentication method of music voice devices based on RF fingerprinting, and to propose an efficient and accurate authentication method combining the time-domain features and frequency-domain features of RF signals through in-depth analysis of RF signal features. The research results indicated that the feature fusion RF fingerprint identification technology is significantly better than the single algorithm, deep learning and neural network methods in terms of accuracy, precision rate, operation efficiency and anti-interference ability. For example, the average accuracy rate of feature fusion was 95.67%, the average precision rate reached 96.14%, the average running time was only 11.34 s, and the interference ability was 0.89. In addition, in the practical application, the technology also achieved excellent results in terms of user experience effect, with an average score of 9.42. The research has practical applications in enhancing the security of music voice devices, protecting user privacy, and improving user experience. It is significant in promoting the development of RF fingerprint identification technology.