A Dynamic Matching Algorithm for Audio Timestamp Identification Using the ENF Criterion

• Published in: IEEE transactions on information forensics and security Vol. 9; no. 7; pp. 1045 - 1055
• Main Authors: Guang Hua, Goh, Jonathan, Thing, Vrizlynn L. L.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.07.2014; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acoustic signal processing; Acoustics; Algorithms; Applied sciences; Computer science; control theory; systems; Correlation; Detection, estimation, filtering, equalization, prediction; Estimation; Exact sciences and technology; Fourier transforms; Frequency estimation; Fundamental areas of phenomenology (including applications); Information, signal and communications theory; Memory and file management (including protection and security); Memory organisation. Data processing; Noise; Physics; Signal and communications theory; Signal resolution; Signal to noise ratio; Signal, noise; Software; Telecommunications and information theory; Time-frequency analysis; Transduction; acoustical devices for the generation and reproduction of sound; audio forensics; timestamp identification; Electric network frequency (ENF); audio authentication; Correlation coefficient; Short term; Fourier transformation; Forensic science; Time stamping; Perception threshold; Duration; Windows; Background noise; Date; Mean square error; Noise level; Experimental result; Electrical network; Authentication; Audio acoustics; Robustness; Signal to noise ratio
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2014.2321228

The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.