Compressing a transmitted signal

  • Thread starter Thread starter granpa
  • Start date Start date
  • Tags Tags
    Signal
AI Thread Summary
The discussion centers on the theoretical possibility of compressing an audio signal by performing a Fourier transform, dividing the frequencies by 10, and then transmitting the signal over reduced bandwidth. While this could be feasible, it would likely result in noticeable loss of signal quality, particularly in the ability to distinguish between closely spaced frequencies. The process would require digitization of the audio signal, and Shannon's theorem indicates limits on compression based on bandwidth and signal-to-noise ratio. Additionally, using techniques like FSK would necessitate narrower filters, which could further degrade the waveform during frequency transitions. Overall, while some compression is possible, it may lead to muddled audio, especially for complex sounds like speech and music.
granpa
Messages
2,268
Reaction score
7
would it be possible in theory to take an audio signal, do an Fourier transform on it, divide all the frequencies by 10, convert it back to the time domain, transmit the resulting signal over 1/10th the bandwidth you would have originally needed, then reverse the process on the other end?

There would be some loss of signal quality and I don't suppose it could be done in real time but wouldn't it at least be possible with something like radio?
 
Engineering news on Phys.org
By performing a Fourier transform on your audio signal, you would first need to digitize the stream because there is no simple analog way of doing it. And so, one of Shannon's theorem applies which gives you an upper limit on the amount of compression you can do. The upper limit also depends on the bandwidth of the system, and the SNR (signal-to-noise ratio)

But with a simple audio signal, whose bandwidth is much smaller than of a simple RF link - is far from the upper limit and so you have room do all kinds of weird compressions on it.
 
I expected that there would be loss of signal quality
but I wonder if it would be noticeable
 
I just read your post again, and if you do as you say, divide by 10 in frequency domain and then take an inverse Fourier of it to reverse the process?

If you do that then your audio will also be divided by 10 in time domain.
 
if anything you would expect the time domain signal to be stretched tenfold but
that would be dividing the frequencies by 10 while stretching the time 10fold.

What I am suggesting is that you divide the frequencies by 10 while leaving the time alone.
 
To make this a little clearer let's use the example of FSK. Let's say you are transmitting data using FSK. What you are suggesting really is no different than using two FSK frequencies at 0.1 times the frequencies originally used. Why can't FSK frequencies be spaced closer together in order to save bandwidth?

In order to gain benefit from frequencies spaced closer together the receiver would need much narrower filters. During the transition from one frequency to another additional sidebands are produced. If a narrower receive filter is used, some of those sidebands will be eliminated making it take longer for the waveform to transition to the other frequency. Because it now takes longer to transition to the other frequency, it is at each frequency for a smaller proportion of the time and taking more time during the transition.

In other words if you input a square wave into an FSK link, as the frequencies are moved closer together and the filter is narrowed, you would see the received waveform approach a sine wave and then diminish in amplitude. You would not be able to retrieve your square wave by doing an IFT.

So the separation of the FSK frequencies and the filter bandwidth are determined primarily by the maximum data rate.
 
well I was thinking mainly about transmitting voice (and maybe even music) and I fully expected to lose some information but I thought that maybe it wouldn't be noticeable.

instead of losing higher or lower frequencies you would just lose the ability to distinguish between frequencies that are close together.
 
I think you would be able to distinguish between two constant frequencies that are close together. What you would lose is the transition between frequencies and changes in amplitude. I imagine speech would sound muddled.
 
Back
Top