missing samples Options

[dave1]

Hi,
I have a question about missing samples, or non-uniform samples. I found out that audio samples can be reconstructed if they are oversampled. My question is can a 20 second oversampled audio file be reconstructed from the first or last 10s of the same recording, or recover a missing audio from what ever that you have. I know that this has something to do with Fourier transform, so, please, can someone explain me how it`s done, or can it be done? I suspect it is possible with simple uniform frequencies, but not with real life audio recordings.
Thanks

[dave1]

Thank you all, those are great answers. So, when you missing bulk samples, i.e. last tenth or last half, doesn`t matter, it is impossible to recover it, but, as @lvqcl pointed out, if you have every second, or forth, or eight sample (the power of two rule applies here, I assume), the recovery of all samples is possible without loss. It seams that, basicaly, it all comes down to sample resolution. I didn`t see the undersampled option, thanks for pointing it out, @pdq. And, of course, corrupted samples are another matter all together.

[greynol]

the power of two rule applies here, I assume

No, it is only because Ivqcl arbitrarily used an oversampling factor that was a power of two.

If you oversampled by something that isn't so conveniently neat and tidy like 96/44.1 then you can't eliminate all but every x*n sample where n is some power of two (or some other integer that is not a power of two!) and retain the information.

This post has been edited by greynol: Jun 11 2012, 19:17

[dave1]

If you oversampled by something that isn't so conveniently neat and tidy like 96/44.1 then you can't eliminate all but every x*n sample where n is some power of two (or some other integer that is not a power of two!) and retain the information.

Do you talking about prime number (dividable only by itself and 1) as a number of samples here? Because in that case you would be wright, and for every integer there would be a remainder which would represent bulk loss of samples and information. I assumed the power of two because of Fast Fourier Transform algorithm which requires that condition. I believed that every sample reconstruction algorithm uses FFT, but I guess I`m wrong.

I`m also curious about the limit of missing samples. How much samples can you throw away before the recovery of those samples becomes unreliable or incomplete?