A Theoretical Architecture of Voiceprint Recognition for Network Security Situational Awareness
Year: 2025
Type: article
Abstract: This paper proposes a theoretical framework for DenseNet-based voiceprint recognition, which incorporates spectrogram enhancement and adaptive histogram equalization to overcome the limitations of conventional methods in feature extraction robustness under noisy conditions. The framework synergistically combines spectral feature enhancement with DenseNet's dense connectivity, achieving both improved feature discriminability and deep feature reuse through: optimized time-frequency representation via enhanced spectrograms, hierarchical feature propagation enabled by dense blocks. Theoretical analysis confirms the framework's capability to maintain recognition stability against acoustic interference, establishing a novel biometric authentication paradigm for cybersecurity situational awareness systems.
DOI: 10.61784/wjit3031
Author: Ping Xia
Institution: Guangzhou College of Technology and Business
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410179101