ENF Detection in Audio Recordings via Multi-Harmonic Combining
Han Liao, Guang Hua, Haijian Zhang · IEEE Signal Processing Letters · 2021
The detection of the electric network frequency (ENF) in digital recordings is an essential step before the subsequent ENF extraction and forensic analysis. In this letter, we extend the state-of-the-art single-tone time-frequency (TF) domain ENF detector to the multi-tone scenario and propose a multi-harmonic combining (MHC) method, exploiting ENF harmonic components for improved detection performance. To exclude the corrupted components interfering rather than contributing to ENF detection, the proposed detector first performs a pre-screening based on the estimated average subband signal-to-noise ratios (SNRs) to exclude interfering components. Then, with the selected harmonic candidates, a second screening process is applied based on the TF test statistics (TSs), i.e., the variances of observed subband traces. After that, the multi-harmonic components are combined to form the final TS, whose sign determines the final decision. The advantages of the proposed method are illustrated via both synthetic analysis and real-world experimental results using the ENF-WHU dataset.