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Are you comparing a BBC documentary to a science fiction show?
* The power company probably does record frequency fluctuations, if for no other reason than to verify proper operation or potential issues. I cannot envision such detailed records being kept for more than a few days time, however, since after that they are of no further use to the power company. Perhaps a record of an anomalous reading may be kept longer, but that would be a serious hit-and-miss situation.
So, we are talking about how terrible it is that theoretically under perfect circumstances there is a slight chance that the power line signal might be used on some calls to determine the time placed... when all that information is available easily and more accurately from the telephone company.
Did you watch the video?
Originally posted by TheRedneck
reply to post by Arbitrageur
Are you comparing a BBC documentary to a science fiction show?
In this case, yes, I am afraid I am. It is disappointing to me that I have to do so, because I felt the BBC was a pretty informative and accurate network... until now.
Here's the deal in a nutshell:
IfTHEN AND ONLY THEN it may be THEORETICALLY possible for that hum to be recorded and matched up to the power company logs.
- you make a phone call
- using a phone which has poor noise-cancelling ability
- from within a few feet of a power line transformer or older appliance that does put out a power line frequency hum
- in an otherwise perfectly silent area
- the power company is maintaining logs of that detail on their power fluctuations*
- the recording is of sufficient quality to detect the hum to that precise level
What they would use this for is for time-stamping video footage take on a hand-held video camera or other recording device which disallows them from easily pin-pointing the time of the recording.
Can't you hear music over the phone? The noise canceling feature doesn't cancel out the sound of music, does it?
The area doesn't have to be otherwise perfectly silent. All you need is sufficient signal to noise ratio at the ~50Hz frequency.
Originally posted by AmberLeaf
Apparently the National Grid runs at 50hz, this signal may not be audible to the human ear in some cases....A slight buzz on a recording can give away the exact time the recording was taken.
Its pretty late here but i wanted to share this video which explains better what im going on about.....
That doesn't exactly add up. First you talk about a time lapse video. Time lapse might be for example 1 frame per 10 seconds.
Originally posted by stormcell
The national grid doesn't run at a constant 50 Hz, it actually varies up and down depending on demands. You can make a timelapse video of street-lights and you'll see the flickering speed up and slow down. The national grid keeps track of this varying speed, so it then becomes possible to determine the time when a video was made.
Originally posted by AmberLeaf
Apparently the National Grid runs at 50hz, this signal may not be audible to the human ear in some cases....A slight buzz on a recording can give away the exact time the recording was taken.
Its pretty late here but i wanted to share this video which explains better what im going on about.....
But the initial conversion to digital format is relevant. In order to digitize a signal, that signal must be broken down into quantized packets, in the process losing some of the original analog data. For voice transmission, or even more difficult signals, sufficient bits can be employed to minimize the information loss, but when the signal component is inaudible that loss can easily be enough to remove any usable information.
The phase shift will affect the resonance of the signal, however, and would cause interference with any power line component introduced, which would cause a slight frequency shift in the reading due to the malformation of the interfering waveforms.
The bandwidth affects the accuracy of the signal. Just as there is more detail in a 10 MPx camera image than in a 1 MPz image, there is more accuracy and thus usable information in a higher bandwidth signal than in a lower bandwidth signal.
Noise can be audible to humans. Noise filtering involves detection of constant signals (such as a 50-Hz hum) and removing that particular frequency, as well as using feedback recognition techniques to remove noise caused by feedback.
This doesn't make any sense. You are using terminology you do not understand.
No matter the combination of resistive, inductive, or capactive elements, if the input is a certain frequency sinusoidal then all voltages, currents, and sum of these elements will also be sinusoidal of that same frequency. Since we are measuring frequency, the fact that different elements will have differing phase angles or may be in resonance is completely irrelevant.
What matters is how much the frequency of the ~50 Hz signal can be expected to vary and the corresponding accuracy required to measure changes in it. The 50 Hz signal is not sending any information, it is just a sinusoidal, therefore it is inaccurate to state a low 'bandwidth' (I'm not even sure this is the right word to use in this context) means less information - it isn't carrying any in the first place.
A 50 Hz hum isn't constant though. It's only there if equipment generating a hum is present. It would only be cancelled if the noise cancelling technology is designed to filter ambient sound and is adaptive.
Originally posted by TheRedneck
The difference in time between identical wave points on a 49.99 Hz and a 50.01 Hz (.02 Hz bandwidth) is approximately 0.000008 seconds. In order to detect this accurately would require a sampling rate of 125kHz. Typical sampling rates are 44.1 kHz, about one-third of the minimum rate required to make a differential measurement based on wavelength and about one-sixth the minimum rate to give reliable results..Thus the audio track cannot be used to provide a peak-to peak, trough-to-trough, or zero-crossing analysis.
The only other way to measure frequency, and the one commonly used, is to compare voltage differentials with respect to time at the zero-crossing points of the wave. This works with sinusoidal wave patterns fairly well, since the maximum differential voltage rates can be predicted from the frequency. That becomes impossible when phase shift is introduced, because the waveform is distorted from a true sine wave and the voltage differentials become impossible to predict without exact knowledge of the waveform itself.
Originally posted by TheRedneck
reply to post by EasyPleaseMe
No. The sampling rate is the rate at which samples of the analog signal are taken and converted into digital data. In order to produce an analog signal (sound) from the digital recording, the sampling rate must be considerably higher than the frequency being sampled. In order to detect the frequency of the digital signal, there must be enough data available to determine which sample is indicative of which wave.
Example: If I tried to sample a 44.1 kHz signal using a 44.1 kHz sampling rate, I would get a flat line indicating no oscillatory signal whatsoever. The reason for this is that each time the signal is sampled, the wavefront is at the exact same position. If I try to sample a 22.05 kHz signal using a 44.1 kHz sampling rate, I could get several different results depending on where the wavefront was when the first sample was taken. If it was at 0°, I would still get a flat line because sine 0° = sine 180° = sine 360°. If it was at 45°, I would get samples corresponding to sine 45°, sine 225°, sine 45° and so on, which would look like a 22.05 kHz signal at 0.707 times the original signal. If the wavefront was at 90°, the resulting sampling would indicate a 22.05 kHz signal at full volume.
That's with a perfect harmonic of the sampling rate, which will never happen in reality. If the frequency being sampled is not a perfect harmonic and is not oscillating far slower than the sampling rate, the result is erroneous data that will not give a reasonable recreation of the original waveform. If the recreated waveform is not reasonably accurate, the frequency of that waveform is questionable at best and totally inaccurate at worst.
The above does not include the number of discrete intervals used in sampling, intervals which act to approximate the levels sampled. This further distorts the wave characteristics.
When the sampling rate is sufficiently higher than the frequency being sampled, each wave period is sampled several times, leading to a more accurate recording of the actual wave. In this case, the errors described above become irrelevant and the quality of the signal is maintained.
Originally posted by TheRedneck
If I take a photo of a certain stretch of highway every five minutes, I can count the number of cars in each photo and get an approximation of how many cars are traveling that stretch of highway for every hour in the day. I can say with some level of certainty that there are, say, twice as many cars on average between 3:00 PM and 4:00 PM on a weekday as there are between 2:00 PM and 3:00 PM on a weekday. I cannot say with certainty how many cars were traveling that highway at 3:56:42 on August 29th 2013 (assuming that does not coincide with a photo), because the number of cars there is constantly changing and I only have limited data to go on. I can only make approximations about periods much longer than five minutes based on averages.
Same principle.
TheRedneck
When the sampling rate is sufficiently higher than the frequency being sampled, each wave period is sampled several times, leading to a more accurate recording of the actual wave. In this case, the errors described above become irrelevant and the quality of the signal is maintained.
Signal decimation – Many digital audio recordings are recorded at high sampling frequencies – e.g., 44100 Hz. To detect the ENF, which is approximately 50 Hz, much lower sampling frequencies are allowed. The audio file is thus decimated to a sampling frequency of 300 Hz, which significantly reduces computational time.
Band pass filtering – The frequencies of interest are around 50 Hz, so the decimated audio file is
digitally band pass filtered from 49.5 Hz to 50.5 Hz to isolate the ENF.
Short Time Fourier Transform (STFT) – In discrete time STFT analysis, a signal is divided into J partly overlapping frames (figure 3) for which, after windowing and zero-padding, the frequency spectrum is calculated via a Discrete Fourier Transform (DFT). The jump H (in samples) between frames determines the time resolution of the final ENF pattern, while the amount of overlap M − H affects its smoothness. In our specific case, we have chosen H = 300 so that the extracted ENF pattern time resolution equals that of the database – i.e., 1 second. Each frame was windowed with a rectangular window and zero-padded by a factor of 4.
Peak frequency estimation – For each frequency spectrum, the frequency with maximum amplitude is estimated. As it is unlikely that this ‘peak frequency’ coincides exactly with a DFT frequency bin, quadratic interpolation around the bin with maximum amplitude is performed. The estimated peak frequency is stored as the ENF value for the corresponding frame, so that we end up with an extracted ENF pattern of J ENF values.
www.forensic.to...
Is responsible for the fluctuation of the frequency of the power consumption. While all power generators operate largely constant at a frequency of 50 Hz, ie 50 cycles per second. But every time, for example, a factory is booted, the generators are heavily loaded - to the power plants to offset the increased demand after a short time. They increase the power, the rotors rotate faster again. And so the system frequency varies in a tiny range from 49.95 to 50.05 hertz - enough for the state police. Officials register the slightest fluctuations, and extends to them a small amount of power. The noise can be heard even when the recording device does not depend on the electrical network, for example when the film is taken with a portable video camera. Often enough, the sound emanating from other electrical equipment in the room to measure the variations in frequency can.
Since July 2010, officials cut the mains frequency, 24 hours a day, is a target database. Across Germany, the Bavarian LKA is a pioneer - this technique is relatively simple. The mains frequency could be measured at each outlet, says Dagmar defeated. "We in Munich have simply recognized as the first genius of this application."
(Translated by Google Translate)
www.sueddeutsche.de...
That's what noise-cancelling circuitry does.
I am puzzled as to why you have ignored all prior discussion in this thread and instead you have insisted that those are the only ways to measure frequency, even though the discrete time Fourier transform is the fundamental basis for all DSP.
C0bzz
I am puzzled as to why you have ignored all prior discussion in this thread and instead you have insisted that those are the only ways to measure frequency, even though the discrete time Fourier transform is the fundamental basis for all DSP.