Blackman–Tukey spectral estimation and electric network frequency matching from power mains and speech recordings

Abstract

Abstract Forensic applications exploit electric network frequency (ENF) as a fingerprint to determine multimedia content authenticity, as well as the time and region of multimedia recording. ENF is present at a nominal frequency of 50/60 Hz and its harmonics. Strong interference due to speech content deteriorates ENF estimation accuracy. Herein, the authors propose a non‐parametric approach for ENF estimation, which incorporates a customised lag window design into the Blackman–Tukey spectral estimation method. Leakage reduction is formulated as a problem of energy maximisation within the main lobe of the spectral window. The proposed approach is compared to state‐of‐the‐art methods for ENF estimation. Maximum correlation coefficient and minimum standard deviation of errors are employed to measure ENF estimation accuracy. Hypothesis testing is performed to determine whether the improvements in ENF estimation accuracy of the proposed approach over the state‐of‐the‐art methods are statistically significant. Experimental results and statistical tests indicate that the proposed approach improves ENF estimation against many state‐of‐the‐art methods.

Keywords

• correlation methods
• correlators
• spectral analysis
• speech processing
• statistical analysis
• statistical testing