If we have a 24 bit input signal at 48kHz is there any benefit to be had processing it at 24/96?
24/48 v 24/96
Started by ●July 29, 2010
Reply by ●July 29, 20102010-07-29
On 29 Jul, 15:50, Dirk Bruere at NeoPax <dirk.bru...@gmail.com> wrote:> If we have a 24 bit input signal at 48kHz is there any benefit to be > had processing it at 24/96?The data sampling parameters at the ADC decide what can be done with the data. There are no benefits to cranking up the processing speed unless you already have a processing chain specifically tuned to deal with 24bit / 96 kHz data. Rune
Reply by ●July 29, 20102010-07-29
On Thu, 29 Jul 2010 06:50:06 -0700 (PDT), Dirk Bruere at NeoPax <dirk.bruere@gmail.com> wrote:>If we have a 24 bit input signal at 48kHz is there any benefit to be >had processing it at 24/96?Possibly. Does your processing include anything that can be considered modulation, such as dynamic range compression? If so, then it generates modulation products that can alias to audible frequencies if the sampling rate is too low. Greg
Reply by ●July 29, 20102010-07-29
On Jul 29, 9:50�am, Dirk Bruere at NeoPax <dirk.bru...@gmail.com> wrote:> If we have a 24 bit input signal at 48kHz is there any benefit to be > had processing it at 24/96?what processing did you have in mind? only linear operations? anything non-linear? if you're planning on some emulation of vacuum tube pre-amps or amps (just to get that "warm sound"), 96 kHz might not be enough. r b-j
Reply by ●July 29, 20102010-07-29
robert bristow-johnson wrote:> if you're planning on some emulation of vacuum tube pre-amps or amps > (just to get that "warm sound"), 96 kHz might not be enough.You don't have to. 0. Approximate nonlinearity by polynomial. 1. Approximate signal by polynomial. 2. Apply nonlinearity and get a polynomial. 3. Compute Fourier from polynomial in closed form. 4. Drop would-be aliased components. 5. Compute inverse Fourier, also closed form. 6. Generate signal back. Vladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com
Reply by ●July 29, 20102010-07-29
On 07/29/2010 10:05 AM, Vladimir Vassilevsky wrote:> > > robert bristow-johnson wrote: > > >> if you're planning on some emulation of vacuum tube pre-amps or amps >> (just to get that "warm sound"), 96 kHz might not be enough. > > You don't have to. > > 0. Approximate nonlinearity by polynomial. > 1. Approximate signal by polynomial. > 2. Apply nonlinearity and get a polynomial. > 3. Compute Fourier from polynomial in closed form. > 4. Drop would-be aliased components. > 5. Compute inverse Fourier, also closed form. > 6. Generate signal back.Yes, but will it sound like the tubes have been dipped in LN2? http://www.cryoset.com/catalog/product_reviews_info.php?products_id=32&reviews_id=6. -- Tim Wescott Wescott Design Services http://www.wescottdesign.com Do you need to implement control loops in software? "Applied Control Theory for Embedded Systems" was written for you. See details at http://www.wescottdesign.com/actfes/actfes.html
Reply by ●July 29, 20102010-07-29
On Jul 29, 1:05�pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:> robert bristow-johnson wrote: > > if you're planning on some emulation of vacuum tube pre-amps or amps > > (just to get that "warm sound"), 96 kHz might not be enough. > > You don't have to. > > 0. Approximate nonlinearity by polynomial. > 1. Approximate signal by polynomial.this assumption is not necessary. we just need to view the signal as bandlimited to the original Nyquist before upsampling.> 2. Apply nonlinearity and get a polynomial.what you get is the signal with images (of some form) going up to N times the original bandlimit, where N is the polynomial order, and some of those images may be folded over, depending on the upsampling ratio.> 3. Compute Fourier from polynomial in closed form.why bother? filtering in the time domain is cheap enough and good enough.> 4. Drop would-be aliased components.and hope that none of them aliases made it back into your original baseband. if they do, you might be fucked.> 5. Compute inverse Fourier, also closed form.i never left the time domain.> 6. Generate signal back.Vlad, i've been here. the polynomial order depends on the nature of the nonlinearity in your processing. and the upsampling ratio, R, depends on the order of the polynomial. let's say, for shits and grins, that a 7th-order polynomial is sufficient to approximate some *static* tube curve that you want to emulate. it turns out that oversampling by 4 suffices because the images above the 4th (the 5th, 6th, and 7th), will, at worst, fold back to be a little higher than 3x, 2x, and 1x times the original bandlimit. since we're gonna filter out them sons-of-bitches out before we downsample, we don't care if they're aliased or not. with 2x oversampling, you can get away with a 3rd-order polynomial, no higher. so the necessary oversampling ratio is R > (N+1)/2 where N is the polynomial order. this, of course, means that we need *good* oversampling so that the spectrum above the original bandlimit remains virtually empty of energy content. anything higher *could* potentially fold back into the basebase and become an audible, nasty, inharmonic alias. r b-j
Reply by ●July 29, 20102010-07-29
On 7/29/2010 1:17 PM, Tim Wescott wrote:> On 07/29/2010 10:05 AM, Vladimir Vassilevsky wrote: >> >> >> robert bristow-johnson wrote: >> >> >>> if you're planning on some emulation of vacuum tube pre-amps or amps >>> (just to get that "warm sound"), 96 kHz might not be enough. >> >> You don't have to. >> >> 0. Approximate nonlinearity by polynomial. >> 1. Approximate signal by polynomial. >> 2. Apply nonlinearity and get a polynomial. >> 3. Compute Fourier from polynomial in closed form. >> 4. Drop would-be aliased components. >> 5. Compute inverse Fourier, also closed form. >> 6. Generate signal back. > > Yes, but will it sound like the tubes have been dipped in LN2? > > http://www.cryoset.com/catalog/product_reviews_info.php?products_id=32&reviews_id=6.And can I sell you a pair of $200/ft speaker cables? Jerry -- Engineering is the art of making what you want from things you can get. �����������������������������������������������������������������������
Reply by ●July 29, 20102010-07-29
robert bristow-johnson wrote:> On Jul 29, 1:05 pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote: > >>robert bristow-johnson wrote: >> >>>if you're planning on some emulation of vacuum tube pre-amps or amps >>>(just to get that "warm sound"), 96 kHz might not be enough. >> >>You don't have to. >> >>0. Approximate nonlinearity by polynomial. >>1. Approximate signal by polynomial. > > this assumption is not necessary. we just need to view the signal as > bandlimited to the original Nyquist before upsampling. > >>2. Apply nonlinearity and get a polynomial. > > > what you get is the signal with images (of some form) going up to N > times the original bandlimit, where N is the polynomial order, and > some of those images may be folded over, depending on the upsampling > ratio. > >>3. Compute Fourier from polynomial in closed form. > > > why bother? filtering in the time domain is cheap enough and good > enough. > > >>4. Drop would-be aliased components. > > > and hope that none of them aliases made it back into your original > baseband. if they do, you might be fucked. > > >>5. Compute inverse Fourier, also closed form. > > > i never left the time domain. > > >>6. Generate signal back. > > > Vlad, i've been here.No, you didn't get the idea. If the signal can be approximated as polynomial x = P(t), and nonlinearity is also polynomial F(x) static, then you can find Nyquist bandlimited Q(t) = F(P(t)) as the closed form solution. No filtering, no upsampling, only algebra. Vladimir Vassilevsky DSP and Mixed Signal Design Consultant http://www.abvolt.com
Reply by ●July 30, 20102010-07-30
On Jul 29, 8:29�pm, Vladimir Vassilevsky <nos...@nowhere.com> wrote:> > No, you didn't get the idea. > > If the signal can be approximated as polynomial x = P(t), and > nonlinearity is also polynomial F(x) static, then you can find Nyquist > bandlimited Q(t) = F(P(t)) as the closed form solution. No filtering, no > upsampling, only algebra. >i now get what you're saying. let's assume that this is real-time and you have the current sample and the L-1 previous samples. to approximate a the recent segment of signal as a polynomial (of order L-1) is a mess if you want to get the traditional coefficients. if you leave it in the Lagrange factored form, it's not so messy, but what can you do with it? running it (in either form) into a static polynomial F(x) is gonna be a mess and will increase the order and the implied bandlimit. just evaluating it for the specific sample times is like sampling that high order polynomial at sample times. but that compound polynomial will have frequency components much greater than you Nyquist frequency unless you do this at the upsampled rate. so the images get folded over into your baseband and you can't do anything about them. so besides messy, i think there is an aliasing problem with the idea. r b-j
