ffmpeg vs. SoX for resampling 
ffmpeg vs. SoX for resampling 
Feb 5 2013, 11:00
Post
#1


Group: Members (Donating) Posts: 171 Joined: 1October 01 From: Doylestown, PA Member No.: 145 
I just found out that TAudioConverter, although it has SoX support, uses ffmpeg for bitdepth changes, resampling, and dither. I don't use those functions very much, but I'm curious if anyone has done a quality comparison between the two.



Feb 13 2013, 22:11
Post
#2


Group: Members Posts: 1826 Joined: 24June 02 From: Catalunya(Spain) Member No.: 2383 
@ saratoga:
As I said, my knowledge of this is average, and I might even use the wrong words sometimes. That said, I'm not sure I agree completely with what you say. A > B does not necesarily equal B > A A sinc filter (which has a sinc function as its impulse response) is a lowpass filter, but a lowpass filter is not necesarily a sinc filter. I don't have the math knowledge to make filters (or understand fully the poles and zeroes), but I understand that the polynomials generated are not just "sinc aproximations". Just like the most basic lowpass filter is not a sinc filter: o0 + (i1o0)*FC You simplified the two methods I described as to using a lowpass filter. In essence, this is true (we want to get a lowpassed signal to avoid aliasing), but I wanted to differentiate the theory from the result. Example: A linear interpolation is an intuitive way to find the value between two points, but it is not based on theory that reconstructs the path that a continuous bandlimited signal would take. In that way, i made a distinction between the sinc method and the decimate/interpolate method because they do have a theory related to sound behind them, but it is not the same theory. (Or, let's say, one is the theory directly, and the other is a derivate of the theory, as in the second one does not necesarily imply a sinc filter, even though it is the ideal one). I can accept that the decimate/interpolate method is akin of doing a fixed point implementation of a floating point one, so in essence, they do the same. But as an implementation, they reach the solution differently. About polynomials, I admit I might have been too quick. I overlooked the math history, but again I was mentioning concrete methods while you mention the concepts on which they are based. Polynomials serve many purposes, and not all of them apply to bandlimited signals. I mentioned graphics, because the word "spline" does describe that, a line (a visual concept). Images are indeed made of samples, but.. what is the equivalent shannon theorem for images? I could accept that images are bandlimited (there's a finite spectrum represented by the sampled image colours, but even then, the RGB points are the representation of the image in the time domain?) 


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