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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.
Author (s): Sanaei, Alireza; Toulson, Rob; Cole, Michael
Affiliation:
Anglia Ruskin University, Cambridge, UK
(See document for exact affiliation information.)
Publication Date:
2014-01-06
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Permalink: https://aes2.org/publications/elibrary-page/?id=17083
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Sanaei, Alireza; Toulson, Rob; Cole, Michael; 2014; Tuning and Optimization of an Electric Network Frequency Extraction Algorithm [PDF]; Anglia Ruskin University, Cambridge, UK; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=17083
Sanaei, Alireza; Toulson, Rob; Cole, Michael; Tuning and Optimization of an Electric Network Frequency Extraction Algorithm [PDF]; Anglia Ruskin University, Cambridge, UK; Paper ; 2014 Available: https://aes2.org/publications/elibrary-page/?id=17083
@article{sanaei2014tuning,
author={sanaei alireza and toulson rob and cole michael},
journal={journal of the audio engineering society},
title={tuning and optimization of an electric network frequency extraction algorithm},
year={2014},
volume={62},
issue={1/2},
pages={25-36},
month={january},}