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dynamic convolution

Type : a naive implementation in C++
References : Posted by Risto Holopainen

Notes :
This class illustrates the use of dynamic convolution with a set of IR:s consisting of exponentially damped sinusoids with glissando. There's lots of things to improve for efficiency.

Code :
#include

class dynaconv
{
public:
// sr=sample rate, cf=resonance frequency,
// dp=frq sweep or nonlinearity amount
dynaconv(const int sr, float cf, float dp);
double operator()(double);

private:
// steps: number of amplitude regions, L: length of impulse response
enum {steps=258, dv=steps-2, L=200};
double x[L];
double h[steps][L];
int S[L];
double conv(double *x, int d);
};



dynaconv::dynaconv(const int sr, float cfr, float dp)
{
for(int i=0; i x[i] = S[i] = 0;

double sc = 6.0/L;
double frq = twopi*cfr/sr;

// IR's initialised here.
// h[0] holds the IR for samples with lowest amplitude.
for(int k=0; k {
double sum = 0;
double theta=0;
double w;
for(int i=0; i {
// IR of exp. decaying sinusoid with glissando
h[k][i] = sin(theta)*exp(-sc*i);
w = (double)i/L;
theta += frq*(1 + dp*w*(k - 0.4*steps)/steps);
sum += fabs(h[k][i]);
}

double norm = 1.0/sum;
for(int i=0; i h[k][i] *= norm;
}
}

double dynaconv::operator()(double in)
{
double A = fabs(in);
double a, b, w, y;
int sel = int(dv*A);

for(int j=L-1; j>0; j--)
{
x[j] = x[j-1];
S[j] = S[j-1];
}
x[0] = in;
S[0] = sel;

if(sel == 0)
y = conv(x, 0);

else if(sel > 0)
{
a = conv(x, 0);
b = conv(x, 1);
w = dv*A - sel;
y = w*a + (1-w)*b;
}

return y;
}

double dynaconv::conv(double *x, int d)
{
double y=0;
for(int i=0; i y += x[i] * h[ S[i]+d ][i];

return y;
}




Comments


Added on : 06/06/05 by Christian[ AT ]savioursofsoul[ DOT ]de
Comment :
You can speed things up by:

a) rewriting the "double dynaconv::conv(double *x, int d)" function using Assembler, SSE and 3DNow routines.

b) instead of this

"else if(sel > 0)
{
a = conv(x, 0);
b = conv(x, 1);
w = dv*A - sel;
y = w*a + (1-w)*b;
}"

you can create a temp IR by fading the two impulse responses before convolution. Then you'll only need ONE CPU-expensive-convolution.

c) this one only works with the upper half wave!

d) only nonlinear components can be modeled. For time-variant modeling (compressor/limiter) you'll need more than this.

e) the algo is proteced by a patent. But it's easy to find more efficient ways, which aren't protected by the patent.

With my implementation i can fold up to 4000 Samples (IR) in realtime on my machine.




Added on : 20/07/05 by correction[ AT ]point[ DOT ]d
Comment :
Correction to d:

d) only time invariant nonlinear components can be modeled; and then adequate memory must be used. Compressors/Limiters can be modelled, but the memory requirements will be somewhat frightening. Time-variant systems, such as flangers, phasors, and sub-harmonic generators (i.e. anything with an internal clock) will need more than this.




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