"For the last seven years, at the Metropolitan Police forensic lab in south London, audio specialists have been continuously recording the sound of mains electricity.
It is an all pervasive hum that we normally cannot hear. But boost it a little, and a metallic and not very pleasant buzz fills the air.
"The power is sent out over the national grid to factories, shops and of course our homes. Normally this frequency, known as the mains frequency, is about 50Hz," explains Dr Alan Cooper, a senior digital forensic practitioner at the Met Police.
Any digital recording made anywhere near an electrical power source, be it plug socket, light or pylon, will pick up this noise and it will be embedded throughout the audio.
This buzz is an annoyance for sound engineers trying to make the highest quality recordings. But for forensic experts, it has turned out to be an invaluable tool in the fight against crime.
While the frequency of the electricity supplied by the national grid is about 50Hz, if you look at it over time, you can see minute fluctuations.
Comparing the unique pattern of the frequencies on an audio recording with a database that has been logging these changes for 24 hours a day, 365 days a year provides a digital watermark: a date and time stamp on the recording.
Philip Harrison, from JP French Associates, another forensic audio laboratory that has been logging the hum for several years, says: "Even if [the hum] is picked up at a very low level that you cannot hear, we can extract this information."
It is a technique known as Electric Network Frequency (ENF) analysis, and it is helping forensic scientists to separate genuine, unedited recordings from those that have been tampered with."
Noisy, muffled, incoherent recordings are an audio engineer’s worst nightmare, but all too often they contain vital evidence in criminal trials. It’s the job of the forensic audio specialist to extract that evidence.
"This is an ENF collector in java for collecting the variation in frequency in the electric network via the audio card with a AC adapter with the correct voltage. It can assist in forensic research for determining the time of the recording."
Electrical network frequency analysis @ Wikipedia.org
"Electrical network frequency (ENF) analysis is a forensic science technique for validating audio recordings by comparing frequency changes in background mains hum in the recording with long-term high-precision historical records of mains frequency changes from a database. In effect the mains hum signal is treated as if it were a time-dependent digital watermark that can help identify when the recording was created, and help detect any edits in the recording. Historical records of main frequency changes are kept on record e.g. by police in the German federal state of Bavaria since 2010.
The technology has been hailed as "the most significant development in audio forensics since Watergate." However, according to a paper by Huijbregtse and Geradts, the ENF technique, although powerful, has significant limitations caused by ambiguity based on fixed frequency offsets during recording, and self-similarity within the mains frequency database, particularly for recordings shorter than 10 minutes.
More recently, researchers demonstrated that the indoor lights such as fluorescent lights and incandescent bulbs vary their light intensity in accordance with the voltage supplied, which in turn depends on the voltage supply frequency. As a result, the light intensity can carry the frequency fluctuation information to the visual sensor recordings in the similar way as the electromagnetic waves from the power transmission lines carry the ENF information to audio sensing mechanisms. Based on this result, researchers demonstrated that visual track from still video taken in an indoor lighting environments also contain ENF traces that can be extracted by estimating the frequency at which ENF will appear in a video as low sampling frequency of video (25–30 Hz) cause significant aliasing. It was also demonstrated in the same research that the ENF signatures from visual stream and the ENF signature from audio stream in a given video should match. As a result, the matching between the two signals can be used to determine if the audio and visual track was recorded together or superimposed later."
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