Tuning and Optimization of an Electric Network Frequency Extraction Algorithm

Sanaei A, Toulson R, Cole M. Tuning and Optimization of an Electric Network Frequency Extraction Algorithm. J Audio Eng Soc. 2014;62(1/2):25-36. doi:10.17743/jaes.2014.0004

@article{Sanaei2014_17083,
  author = {Sanaei, Alireza and Toulson, Rob and Cole, Michael},
  title = {{Tuning and Optimization of an Electric Network Frequency Extraction Algorithm}},
  journal = {Journal of the Audio Engineering Society},
  volume = {62},
  number = {1/2},
  pages = {25--36},
  year = {2014},
  month = jan,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2014.0004},
  url = {https://doi.org/10.17743/jaes.2014.0004}
}

TY  - JOUR
TI  - Tuning and Optimization of an Electric Network Frequency Extraction Algorithm
AU  - Sanaei, Alireza
AU  - Toulson, Rob
AU  - Cole, Michael
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 62
IS  - 1/2
SP  - 25
EP  - 36
PY  - 2014
DA  - 2014/01/06
DO  - 10.17743/jaes.2014.0004
UR  - https://doi.org/10.17743/jaes.2014.0004
PB  - Audio Engineering Society
LA  - en
AB  - By analyzing the frequency of low-level power line energy (Electrical Network Frequency, ENF) that has leaked into an audio recording, forensic researchers have been able to find the date and time when the recordings were made. This research implements an enhanced ENF extraction algorithm and evaluates the key parameters needed for optimizing its accuracy, such as sampling frequency, analysis window size, window increment, and the choice of harmonic components. For example, the use of odd harmonics, specifically the 5th, 7th, and 9th, for determining frequency is shown to create a better match to the reference database. Unlike current approaches to ENF analysis that use a combination of analog and digital technology, this approach is purely digital with the resulting benefits of reduced noise floor and flexible software algorithms.
ER  -