> What happens when you start combining waves together to create more complex signals Aliasing is a linear effect. If L is an operator that aliases at some fre

> What happens when you start combining waves together to create more complex signals Aliasing is a linear effect. If L is an operator that aliases at some frequency, L(Aa(t) + Bb(t))=AL(a(t))+BL(b(t)). Aliasing is in fact multiplicative! Any time you wish to understand aliasing, you really want write the signal multiplied by a dirac comb (a train of equally spaced infinitely narrow pulses). If you wish to understand nonfinite sampling widths, it's effectively convolution with the shape of sampling pulse... which looks like low-pass filtering. Ya dig? This is the frequency on your scope that's stupidly above the sampling rate. Why's my scope have a bazillion ghz bandwidth when it only samples at 4 samples oer week? It's because it's real good at getting you that signal, aliased down into the sampled signal. > what effect does aliasing really have on an audio signal PRECISELY the same effect it does on video, except in one dimension, and you haven't understood aliasing until you've understood it in one dimension. > Is is possible to show that such information either isn't lost It's lost. Completely... if done properly. First you need to understand Whittaker-shannon interpolation. The result of which is that a CONTINUOUS BAND LIMITED signal is PERFECTLY represented by a train of discrete samples. This is WEIRD. Really weird. Prove it to yourself. It's VERY important in sampling. Say you then perfectly sample a signal with harmonic content ALL over the place. EVERYTHING gets mirrored down into your view, if the dirac comb has infinitely narrow pulses. So at the least you lose frequency information! You only know the original frequencies modulo the nyquist frequency! If you sample CORRECTLY, you band pass filter your incoming signal so there's NOTHING above the nyquist frequency, then you have a perfect digital representation of your original signal. If you wanna understand signal processing, you really can't use your intuition. Practice understanding things in the frequency domain and time domain, and generally there's only one proper basis to understand a phenomenon. Filtering is best understood in the frequency domain, for instance. If you try to understand filtering in the time domain, you might begin to think that it bounds the speed at which the signal can vary but that's not a useful intuition. Aliasing is best understood in the frequency domain, since it moves stuff down in frequency mod the nyquist frequency. If you try to build an intuition in the time domain, you'll think that there's some signal lost when you multiply by zero between pulses... and you'll guess at what it was and think "distortion" or whatever but what's lost is just WHERE each frequency is... so be very careful about what basis you choose!