Can you tell when an audio recording was made, down to the second, just by the electrical background hum? What sounds like a science fiction fantasy is actually real.

Today, The Private Citizen is taking a look at a fascinating digital forensics technique that’s pretty new and quite astounding: electrical network frequency analysis.


This podcast was recorded with a live audience on my Twitch channel. Details on the time of future recordings can usually be found on my personal website. Recordings of these streams get saved to a YouTube playlist for easy watching on demand after the fact.


What is Alternating Current?

To understand how electrical network frequency (ENF) analysis works, one needs to understand what alternating current is. There are two forms of electricity we use in our daily lives: direct current (DC) and alternating current (AC).

In a DC circuit, electrical charge only flows in one direction. In an AC circuit, the charge alternates from positive to negative:

Many sources of electricity, most notably electromechanical generators, produce AC current with voltages that alternate in polarity, reversing between positive and negative over time. An alternator can also be used to purposely generate AC current. In an alternator, a loop of wire is spun rapidly inside of a magnetic field. This produces an electric current along the wire. As the wire spins and periodically enters a different magnetic polarity, the voltage and current alternate on the wire. This current can change direction periodically, and the voltage in an AC circuit also periodically reverses because the current changes direction.

AC comes in several forms, as long as the voltage and current are alternating. If an AC circuit is hooked up to an oscilloscope and its voltage is plotted over time, you are likely to see several different waveforms such as sine, square, and triangle – sine is the most common waveform and the AC in most mains-wired buildings have an oscillating voltage in the sine wave form.

Why is this useful?

AC is most commonly found in mains-wired buildings such as homes and offices. This is because generating and transporting an AC current across long distances is relatively easy. At high voltages of over 110kV, less energy is lost in power transmission. At higher voltages, lower currents are produced, and lower currents generate less heat in the power line due to a lower level of resistance. This therefore means less energy lost as heat. AC currents can be converted to and from high voltages with ease by using transformers.

An effect of electromagnetism (known as “mutual induction”), where two or more coils of wire are placed so that the changing magnetic field in one coil induces a voltage in the other, can be used to make a device called a transformer. If there are two mutually inductive coils and one is energized with AC, an AC voltage will be created in the other coil. The fundamental use of a transformer is stepping voltage up or down from the powered coil to the unpowered coil. This provides AC an advantaged well above DC in the realm of power distribution because transmitting electrical power over long distances is a lot more efficient with higher, stepped-up voltages and smaller, stepped-down currents. Before reaching power outlets, voltage is stepped back down and current is stepped back up. This type of transformer technology has made long-range electric power distribution efficient and practical. Without transformers, it would be far too costly to construct power systems in their current long-distance form. And, because mutual inductance relies on changing magnetic fields, transformers only work with AC.

AC is also great for use in electric motors because motors and generators are the same device. The only difference between a generator and a motor is that a motor converts electrical energy into mechanical energy. These motors are used in all kinds of appliances like refrigerators, washing machines, and dishwashers.

c.f.: The War of the Currents

What is Mains Hum?

One issue of systems using alternating current is what we call mains hum.

Mains hum is a sound associated with alternating current which is twice the frequency of the mains electricity. The fundamental frequency of this sound is usually double that of fundamental 50/60 Hz, i.e. 100/120 Hz, depending on the local power-line frequency.

Which is 60 Hz in the US and 50 Hz in Europe and most of the rest of the world. Funnily, Japan has both frequencies.

Because of the presence of mains current in mains-powered audio equipment as well as ubiquitous AC electromagnetic fields from nearby appliances and wiring, 50/60 Hz electrical noise can get into audio systems, and is heard as mains hum from their speakers. Mains hum may also be heard coming from powerful electric power grid equipment such as utility transformers, caused by mechanical vibrations induced by magnetostriction in the magnetic cores. Onboard aircraft (or spacecraft) the frequency heard is often higher pitched, due to the use of 400 Hz AC power in these settings because 400 Hz transformers are much smaller and lighter.

There are actually many different versions of electric hum:

Electric hum around transformers is caused by stray magnetic fields causing the enclosure and accessories to vibrate. Magnetostriction is a second source of vibration, in which the core iron changes shape minutely when exposed to magnetic fields. The intensity of the fields, and thus the “hum” intensity, is a function of the applied voltage. Because the magnetic flux density is strongest twice every electrical cycle, the fundamental “hum” frequency will be twice the electrical frequency. Additional harmonics above 100/120 Hz will be caused by the non-linear behavior of most common magnetic materials.

Around high-voltage power lines, hum may be produced by corona discharge.

In the realm of sound reinforcement (as in public address systems and loudspeakers), electric hum is often caused by induction. This hum is generated by oscillating electric currents induced in sensitive (high gain or high impedance) audio circuitry by the alternating electromagnetic fields emanating from nearby mains-powered devices like power transformers. The audible aspect of this sort of electric hum is produced by amplifiers and loudspeakers.

The other major source of hum in audio equipment is shared impedances; when a heavy current is flowing through a conductor (a ground trace) that a small-signal device is also connected to. All practical conductors will have a finite, if small, resistance, and the small resistance present means that devices using different points on the conductor as a ground reference will be at slightly different potentials. This hum is usually at the second harmonic of the power line frequency (100 Hz or 120 Hz), since the heavy ground currents are from AC to DC power supplies that rectify the mains waveform.

c.f.: Ground loop

In vacuum tube equipment, one potential source of hum is current leakage between the heaters and cathodes of the tubes. Another source is direct emission of electrons from the heater, or magnetic fields produced by the heater. Tubes for critical applications may have the heater circuit powered by direct current to prevent this source of hum.

How Does ENF Analysis Work?

Electrical network frequency analysis exploits a specific aspect of mains hum, i.e. the hum introduced by the utility company’s systems themselves, to fingerprint audio recordings.

Electrical network frequency analysis is an audio forensics 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, detect edits in the recording, or disprove tampering of a recording. Historical records of mains frequency changes are kept on record, e.g., by police in the German federal state of Bavaria since 2010 and the United Kingdom Metropolitan Police since 2005.

In the UK, police reportedly uses this technique in a fully automated process.

The technique has limitations, 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.

ENF might also be applicable to video sources:

More recently, researchers demonstrated that 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 a 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 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 the visual stream and the ENF signature from the 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 were recorded together or superimposed later.

Tom Scott recorded a video on how ENF analysis works that shows how it is done in the real world.

Producer Feedback

Many people have told me they are happy I am back at my desk and continuing to produce podcasts. As I told nekr0z on the forum: Thanks for telling me this once in a while. It gives me the energy to continue with this show.

Juhan Sonin from MIT, whose talk on owning your own medical data I covered in episode 57, wrote me a very nice email while I was on holiday. Apparently, he had discovered the episode and thanked me for it. He’s continuing his efforts on opensourcehealthcare.org, which seems like a very laudable initiative.

If you have any thoughts on the things discussed in this or previous episodes, please join our forum and compare notes with other producers. You can also contact me in several other, more private ways.

If you are writing in from Russia, you might want to use my whistleblower contact form.

Toss a Coin to Your Podcaster

I am a freelance journalist and writer, volunteering my free time because I love digging into stories and because I love podcasting. If you want to help keep The Private Citizen on the air, consider becoming one of my Patreon supporters.

You can also support the show by sending money to via PayPal, if you prefer.

This is entirely optional. This show operates under the value-for-value model, meaning I want you to give back only what you feel this show is worth to you. If that comes down to nothing, that’s OK with me. But if you help out, it’s more likely that I’ll be able to keep doing this indefinitely.

Thanks and Credits

I’d like to credit everyone who’s helped with any aspect of this production and thus became a part of the show. I am thankful to the following people, who have supported this episode through Patreon and PayPal and thus keep this show on the air:

Galteran, Rhodane the Insane, Steve Hoos, Butterbeans, Michael Small, 1i11g, Jonathan M. Hethey, Michael Mullan-Jensen, Dave, Jaroslav Lichtblau, Jackie Plage, Sandman616, ikn, Bennett Piater, Vlad, Rizele, avis, Joe Poser, Dirk Dede, Kai Siers, Fadi Mansour, David Potter, Mika, Cam, MrAmish, RJ Tracey, Captain Egghead, Rick Bragg, RikyM, Barry Williams, Jonathan, Robert Forster, astralc, krunkle, Superuser, D, Florian Pigorsch and Eric Le Lay.

Many thanks to my Twitch subscribers: Mike_TheDane, pkeymer, BaconThePork, redeemerf, l_terrestris_jim, Galteran, jonathane4747 and indiegameiacs.

I am also thankful to Bytemark, who are providing the hosting for this episode’s audio file.

Podcast Music

The show’s theme song is Acoustic Routes by Raúl Cabezalí. It is licensed via Jamendo Music. Other music and some sound effects are licensed via Epidemic Sound. This episode’s ending song is The Silver Lining by Hara Noda.