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Comment: Re:It IS FLAC (Score 1) 399

Add to the list of things you don't understand: Nyquists theorem.

Educational exercise 1: create 128 random Y values uniformly spaced along the X axis. Delete every other one and fourier transform it. Now pad this out to 2x the frequency and inverse fourier transform it. You get 128 Y values with the original spacing. But they are not the same points you started with.

Educational exercise 2: Create a sine wave at 3.14159 oscilations per second. Sample this at 16 samples per second for 1 second. throw away the second half so you have 8 samples. Fourier transform this. First observe there is no peak at 3.1459 hz because there's no discrete frequency there. Now inverse FT this. You dont get the original 16 points.

Comment: Re:It IS FLAC (Score 1) 399

The greatest time in that articel is spent claiming that 192Khz is overkill because everything above 20Khz is unhearable. He shows how a square looking waveform has all the right spectral components in the 20Khz range and so therefore it it is not missing anything. This is fourier and nyquist type argument that assumes linearity.

as you put it F( a+b) = F(a) + F(b). When this is true then it's as he said. But if F(a+b) != F(a) + F(b) then you need more than 20Khz to describe the spectrum.

I'm not saying 192Khz is the right thing. I'm just say the entire argument in the article is assuming linearity to draw the conclusions that the 0-20Khz spectrum contains all the information you can hear.

In fact we already know that ears are not linear. This is in fact how some compression algorithms function. They know that as it gets loud that you can't hear quieter frequencies as efficiently so they are removed. This is an example that actually works in the opposite direction-- that there's less information needed. However it supports the notion that describing everything by spectral analysis is wrong when things are linear.

You said, well it's just a change of basis. Sort of. How tightly you want to sample has th be determined first. This is what actually sets the bases that the analysis is going to be changing between. A given point spacing in time for a given lenght of time forces the interval over which the fourier transform exists. Conversely if you insist that the highes frequency is 20K (or 40K for nyquist) then you have fixed the time interval of the sampling. You are then blind to any point in the intervals between which is where the non-linear effects could, conceivebly, hide.

Comment: Re:It IS FLAC (Score 1) 399

Apparently this link hasn't been posted enough times yet. It addresses both your first question (partially) and your second question (in huge detail).

The video you're comparing to is being treated no better than audio. It's simply that human eyes are much better than human ears, so to give a comparable experience much higher bitrates are needed for video than audio.

What all these linear analyses assume is that hearing is a linear process. If its non linear then these analyses are incorrect.

Comment: Re:It IS FLAC (Score 5, Informative) 399

Pono music is an ecosystem to sell music in FLAC audio file format: 1) production of FLAC files from existing recordings, 2) a dedicated player, and 3) a web store to sell FLAC files.

The problem with FLAC is how does one get FLAC? you could use your own encoder to record a CD in FLAC. But then you just have CD quality Why not reach back to the studio quality if you are going the FLAC route?. Cause you don't have access to that. But now you do-- the PONO ecosystem does that. And if you wanted to play that FLAC file, well your mp3 player might not play it and if it does it probably has a lot less memory than you would like. soe PONO players are chubbier in memory. And finally what if you are one of those people who likes to roll there own and prefers to just buy it pre recorded. Well agains the PONO ecosystem is there for you.
