Sinc Filter | Dirk Mittler's Blog

There has been some confusion about the Sinc-Filter.

I have read descriptions about the Sinc-Filter somewhere, which predicted that it would become unstable, if the frequency of the input stream, happened to correspond to the spacing, between its non-zero coefficients. As far as I can tell, this prediction was based on a casual inspection of the Sinc Function, but overlooks something which is easy to overlook about it. This case also happens to correspond, to the input stream having a frequency equal to the Nyquist Frequency, of certain practical applications, such as over-sampling.

The Sinc Function has zero-crossings at regular intervals, including the center-point, where its coefficient is stated as being equal to (1.0) . This happens because the value at the center-point, is the solution to a limit equation, that corresponds to (0/0) .

This center coefficient is symmetrically flanked by two positive ones, one of which is only positive, because it forms as a division of the sine of x by the corresponding negative value of x. At frequencies below the Nyquist Frequency, the sum of their products starts to reinforce the center element. Above Nyquist, they start to cancel the product with the center coefficient.

This can be complicated to plot using Computer Algebra Systems, because plotting functions are always numerical, and at (x=0), there is no numerical solution (only the Algebraic solution given lHôpitals Rule). So, a CAS typically needs to have the Sinc Function defined as a special case, to be able to plot it, otherwise requiring a complex workaround.

So it is possible that the frequency of the incoming stream aligns to the spacing between the maxima and minima of the Sinc Function. If that happens, there are two behaviors to bear in mind:

The peak of the input stream could be aligned with the center-point. In that case, all the other waves will have zero-crossings, where the Sinc Function has maxima. The fact that the single input-sample seems to produce (1.0) as the output amplitude, is due to how the function is frequently normalized for practical use. According to that, maximum output should reach (2.0) at a frequency of zero…
The input stream could have a zero-crossing, at the center-point of the Sinc Function, so that its product from there should equal (0.0) . In that case, the input stream will have positive peaks on one side of the center-point, that all correspond to negative peaks on the other side of the center-point. According to that, the instantaneous output should equal (0.0) .

All of this would suggest to me, that the Sinc-Filter will work properly.

One way in which people can misinterpret the plot of the curve, would be to notice it has a positive peak in the center, to notice that after a zero-crossing, it forms two negative peaks, and then to conclude that those negative peaks are also the two closest non-zero coefficients to the center.

Continue reading There has been some confusion about the Sinc-Filter.

I feel that standards need to be reestablished.

When 16-bit / 44.1kHz Audio was first developed, it implied a very capable system for representing high-fidelity sound. But I think that today, we live in a pseudo-16-bit era. Manufacturers have taken 16-bit components, but designed devices which do bot deliver the full power or quality of what this format once promised.

It might be a bit of an exaggeration, but ~~I would say~~ that out of those indicated 16 bits of precision, the last 4 are not accurate. And one main reason this has happened, is due to compressed sound. Admittedly, signal compression – which is often a euphemism for data reduction – is necessary in some areas of signal processing. But one reason fw data-reduction was applied to sound, had more to do with dialup-modems and their lack of signal-speed, and with the need to be able to download songs onto small amounts of HD space, than it served any other purpose, when the first forms of data-reduction were devised.

Even though compressed streams caused this, I would not say that the solution lies in getting rid of compressed streams. But I think that a necessary part of the solution would be consumer awareness.

If I tell people that I own a sound device, that it uses 2x over-sampling, but that I fear the interpolated samples are simply generated as a linear interpolation of the two adjacent, original samples, and if those people answer “So what? Can anybody hear the difference?” Then this is not an example of consumer awareness. I can hear the difference between very-high-pitch sounds that are approximately correct, and ones which are greatly distorted.

Also, if we were to accept for a moment that out of the indicated 16 bits, only the first 12 are accurate, but there exist sound experts who tell us that by dithering the least-significant bit, we can extend the dynamic range of this sound beyond 96db, then I do not really believe that those experts know any less about digital sound. Those experts have just remained so entirely surrounded by their high-end equipment, that they have not yet noticed the standards slip, in other parts of the world.

Also, I do not believe that the answer to this problem lies in consumers downloading 24-bit, 192kHz sound-files, because my assumption would again be, that only a few of those indicated 24 bits will be accurate. I do not believe Humans hear ultrasound. But I think that with great effort, we may be able to hear 15-18kHz sound from our actual playback devices again – in the not-so-distant future.

Continue reading I feel that standards need to be reestablished.

About The Applicability of Over-Sampling Theory

One fact which I have described in my blog, is that when Audio Engineers set the sampling rate at 44.1kHz, they were taking into account a maximum perceptible frequency of 20kHz, but that if the signal was converted from analog to digital format, or the other way around, directly at that sampling rate, they would obtain strong aliasing as their main feature. And so a concept which once existed was called ‘over-sampling’, in which then, the sample-rate was quadrupled, and by now, could simply be doubled, so that all the analog filters still have to be able to do, is suppress a frequency which is twice as high, as the frequencies which they need to pass.

The interpolation of the added samples, exists digitally as a low-pass filter, the highest-quality variety of which would be a sinc-filter.

All of this fun and wonderful technology has a main weakness. It actually needs to be incorporated into the devices, in order to have any bearing on them. That MP3-player, which you just bought at the dollar-store? It has no sinc-filter. And therefore, whatever a sinc-filter would have done, gets lost on the consumer.

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Guessing at the Number of Coefficients Filters Might Need

There probably exist Mathematically-more-rigorous ways to derive the following information. But just in order to be able to understand concepts clearly, I often find that I need to do some estimating, that will give some idea, of how many zero-crossings, for example, a Sinc Filter should realistically have, on each side of its center sample. Or, of what kind of cutoff-performance the low-pass part of a Daubechies Wavelet will have, If it only has 8 coefficients…

If the idea is accepted that a low-pass filter is supposed to be of some type, based on the ‘Sinc Function’, including filters that only have 2x / 1-octave over-sampling, then a question which Electronics Experts will face, is what number of zero-crossings is appropriate. This question is especially difficult to find a precise answer to, because the series does not converge. It is a modified series of the form Infinite Sum (1/n) .

Just to orient ourselves within the Sinc Function when applied this way, the center sample is technically one of the zero-crossings, but is equal to 1, because it has the only coefficient of the form (0/0). After that, each coefficient twice removed is a zero-crossing, and the coefficients displaced from those are the standard non-zero examples.

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