学术文献库

In the field of information forensics, many emerging problems involve a critical step that estimates and tracks weak frequency components in noisy signals. It is often challenging for the prior art of frequency tracking to i)achieve a high accuracy under noisy conditions, ii)detect and track multiple frequency components efficiently, or iii)strike a good trade-off of the processing delay versus the resilience and the accuracy of tracking. To address these issues, we propose Adaptive Multi-Trace Carving (AMTC), a unified approach for detecting and tracking one or more subtle frequency components under very low signal-to-noise ratio (SNR) conditions and in near real time. AMTC takes as input a time-frequency representation of the system's preprocessing results (such as the spectrogram), and identifies frequency components through iterative dynamic programming and adaptive trace compensation. The proposed algorithm considers relatively high energy traces sustaining over a certain duration as an indicator of the presence of frequency/oscillation components of interest and track their time-varying trend. Extensive experiments using both synthetic data and real-world forensic data of power signatures and physiological monitoring reveal that the proposed method outperforms representative prior art under low SNR conditions, and can be implemented in near real-time settings. The proposed AMTC algorithm can empower the development of new information forensic technologies that harness very small signals.