Determination of time of recording with Electric Network Frequency (ENF)
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NEDERLANDSFORENSISCHINSTITUUT
Nederlands Forensisch Instituut Laan van Ypenburg 6, 2497 GB Den Haag
Determination of time of recording with Electric Network Frequency (ENF)
M.P. Huijbregtse BS, Zeno Geradts PhDAAFS Denver 2009
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Outline
• Introduction ENF • Implications• Research• Conclusions
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Introduction
Electricity network• Alternating current • Frequency
• Europe 50 Hz • United States 60 Hz
Introduction Implications Research Conclusions
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Introduction
Introduction Implication Research Conclusions
ENF pattern
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Introduction – prior research• C. Grigoras, Digital audio recording
analysis – the electric network frequency criterion, International Journal of Speech Language and the Law, vol. 12, no. 1, pp. 63-76 (2005)
• M. Kajstura, A Trawinska, J. Hebestreit, Application of the Electrical Network Frequency (ENF) Criterion – A case of a Digital Recording, Forensic Science International, vol. 155, pp. 165-171 (2005)
• A.J. Cooper, The electric network frequence (ENF) as an aid to authenticating forensic digital audio recordings – an automated approach, Conference paper, AES 33rd International Conference, USA (2008)
Introduction Implication Research Conclusions
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Introduction
Introduction Implication Research Conclusions
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Introduction
Introduction Implication Research Conclusions
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Introduction
• Conditions:• ENF pattern is the same in the whole
network• ENF pattern is “unique”• ENF pattern can be filtered from a
recorder
Introduction Implication Research Conclusions
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Implications – build a database
Introduction Implication Research Conclusions
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Implications – Build database
Introduction Implication Research Conclusions
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Implications Build a database
Introduction Implication Research Conclusions
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Software
• Matlab• Java- applet for collecting,
developed by us : www.sourceforge.net enf collector
Introduction Implication Research Conclusions
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Software
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Implications - filter
• Downsampling naar 300 Hz• Bandpass filteren rond 50 Hz• Spectrogram maken• Piek frequenties bepalen
Introduction Implication Research Conclusions
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Implications – Filter signalSpectrogram:
Introduction Implication Research Conclusions
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Implications – filter signal
Introduction Implication Research Conclusions
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Implications – filter signal
Introduction Implication Research Conclusions
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Research - Questions
Introduction Implications Research Conclusions
• How unique is an ENF pattern ?• Is it possible to determine the time of
recording ?
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Implication
Introductie Praktisch Onderzoek Conclusies
Matching root mean square:
L
nkykxne
L
k
1
2]1[][][
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Implication - Uniqueness
Introduction Implications Research Conclusions
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Implication - Uniqueness
Length ENF pattern (seconds) Limit (Hz)
60 0.0007
120 0.0015
240 0.0020
420 0.0035
600 0.0040
1800 0.0050
3600 0.0080
Introduction Implications Research Conclusions
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Implication best match• Recording of 3600 seconds:
0.053 Hz < 0.080 Hz
• Recording of 600 seconds:0.047 Hz > 0.040 Hz (!)
• Recording of 60 seconds :0.015 Hz > 0.007 Hz (!)
Introduction Implications Research Conclusions
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Implication – Best match
Introduction Implications Research Conclusions
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Implication – Best match
Introduction Implications Research Conclusions
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Second method
yx
L
k
L
ynkyxkxnr
)1(
)]1[)(][(][ 1
Correlationcoefficiënt:
Introduction Implications Research Conclusions
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Implication – best match
Introduction Implications Research Conclusions
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Conclusions• As in literature:
• ENF pattern is the same in the whole network
• ENF pattern can be extracted from network
• Suggestions:• Matching of short recording (< 600
seconds) false hits are possible• Matching with the correlation coefficent
better than root mean square errorIntroduction Implications Research Conclusions
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Future research
• Different recorders• (MP3) compression• Improvement of the current
method