Some realizations about Digital Signal Processing

One of the realizations which I’ve just come across recently, about digital signal processing, is that apparently, when up-sampling a digital stream twofold, just for the purpose of playing it back, simply to perform a linear interpolation, to turn a 44.1kHz stream into an 88.2kHz, or a 48kHz stream into a 96kHz, does less damage to the sound quality, than I had previously thought. And one reason I think this is the factual realization that to do so, really achieves the same thing that applying a (low-pass) Haar Wavelet would achieve, after each original sample had been doubled. After all, I had already said, that Humans would have a hard time being able to hear that this has been done.

But then, given such an assumption, I think I’ve also come into more realizations, of where I was having trouble understanding what exactly Digital Signal Processors do. It might be Mathematically true to say, that a convolution can be applied to a stream after it has been up-sampled, but, depending on how many elements the convolution is supposed to have, whether or not a single DSP chip is supposed to decode both stereo channels or only one, and whether that DSP chip is also supposed to perform other steps associated with playing back the audio, such as, to decode whatever compression Bluetooth 4 or Bluetooth 5 have put on the stream, it may turn out that realistic Digital Signal Processing chips just don’t have enough MIPS – Millions of Instructions Per Second – to do all that.

Now, I do know that DSP chips exist that have more MIPS, but then those chips may also measure 2cm x 2cm, and may require much of the circuit-board they are to be soldered in to. Those types of chips are unlikely to be built-in to a mid-price-range set of (Stereo) Bluetooth Headphones, that have an equalization function.

But what I can then speculate further is that some combination of alterations of these ideas should work.

For example, the convolution that is to be computed could be computed on the stream before it has been up-sampled, and it could then be up-sampled ‘cheaply’, using the linear interpolation. The way I had it before, the half-used virtual equalizer bands would also accomplish a kind of brick-wall filter, whereas, to perform the virtual equalizer function on the stream before up-sampling would make use of almost all the bands, and doing it that way would halve the amount of MIPS that a DSP chip needs to possess. Doing it that way would also halve the frequency linearly separating the bands, which would have created issues at the low end of the audible spectrum.

Alternatively, implementing a digital 9- or 10-band equalizer, with the
bands spaced an octave apart, could be achieved after up-sampling, instead of before up-sampling, but again, much more cheaply in terms of computational power required.


How certain signal-operations are not convolutions.

One concept that exists in signal processing, is that there could be a definition of a filter, which is based in the time-domain, and that this definition can resemble a convolution. And yet, a derived filter could no longer be expressible perfectly as a convolution.

For example, the filter in question might add reverb to a signal recursively. In the frequency-domain, the closer two frequencies are, which need to be distinguished, the longer the interval is in the time-domain, which needs to be considered before an output sample is computed.

Well, reverb that is recursive would need to be expressed as a convolution with an infinite number of samples. In the frequency-domain, this would result in sharp spikes instead of smooth curves.

I.e., If the time-constant of the reverb was 1/4 millisecond, a 4kHz sine-wave would complete within this interval, while a 2kHz sine-wave would be inverted in phase 180⁰. What this can mean is that a representation in the frequency-domain may simply have maxima and minima, that alternate every 2kHz. The task might never be undertaken to make the effect recursive.

(Last Edited on 02/23/2017 … )

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