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#include <math.h>
#include <samplerate.h>
#include "ardour/types.h"
#ifndef __interpolation_h__
#define __interpolation_h__
namespace ARDOUR {
class Interpolation {
protected:
double _speed, _target_speed;
// the idea is that when the speed is not 1.0, we have to
// interpolate between samples and then we have to store where we thought we were.
// rather than being at sample N or N+1, we were at N+0.8792922
std::vector<double> phase;
public:
Interpolation () { _speed = 1.0; _target_speed = 1.0; }
void set_speed (double new_speed) { _speed = new_speed; _target_speed = new_speed; }
void set_target_speed (double new_speed) { _target_speed = new_speed; }
double target_speed() const { return _target_speed; }
double speed() const { return _speed; }
void add_channel_to (int input_buffer_size, int output_buffer_size) { phase.push_back (0.0); }
void remove_channel_from () { phase.pop_back (); }
void reset () {
for (size_t i = 0; i <= phase.size(); i++) {
phase[i] = 0.0;
}
}
};
// 40.24 fixpoint math
#define FIXPOINT_ONE 0x1000000
class FixedPointLinearInterpolation : public Interpolation {
protected:
/// speed in fixed point math
uint64_t phi;
/// target speed in fixed point math
uint64_t target_phi;
std::vector<uint64_t> last_phase;
// Fixed point is just an integer with an implied scaling factor.
// In 40.24 the scaling factor is 2^24 = 16777216,
// so a value of 10*2^24 (in integer space) is equivalent to 10.0.
//
// The advantage is that addition and modulus [like x = (x + y) % 2^40]
// have no rounding errors and no drift, and just require a single integer add.
// (swh)
static const int64_t fractional_part_mask = 0xFFFFFF;
static const Sample binary_scaling_factor = 16777216.0f;
public:
FixedPointLinearInterpolation () : phi (FIXPOINT_ONE), target_phi (FIXPOINT_ONE) {}
void set_speed (double new_speed) {
target_phi = (uint64_t) (FIXPOINT_ONE * fabs(new_speed));
phi = target_phi;
}
uint64_t get_phi() { return phi; }
uint64_t get_target_phi() { return target_phi; }
uint64_t get_last_phase() { assert(last_phase.size()); return last_phase[0]; }
void set_last_phase(uint64_t phase) { assert(last_phase.size()); last_phase[0] = phase; }
void add_channel_to (int input_buffer_size, int output_buffer_size);
void remove_channel_from ();
nframes_t interpolate (int channel, nframes_t nframes, Sample* input, Sample* output);
void reset ();
};
class LinearInterpolation : public Interpolation {
protected:
public:
nframes_t interpolate (int channel, nframes_t nframes, Sample* input, Sample* output);
};
#define MAX_PERIOD_SIZE 4096
/**
* @class SplineInterpolation
*
* @brief interpolates using cubic spline interpolation over an input period
*
* Splines are piecewise cubic functions between each samples,
* where the cubic polynomials' values, first and second derivatives are equal
* on each sample point.
*
* Those conditions are equivalent of solving the linear system of equations
* defined by the matrix equation (all indices are zero-based):
* A * M = d
*
* where A has (n-2) rows and (n-2) columns
*
* [ 4 1 0 0 ... 0 0 0 0 ] [ M[1] ] [ 6*y[0] - 12*y[1] + 6*y[2] ]
* [ 1 4 1 0 ... 0 0 0 0 ] [ M[2] ] [ 6*y[1] - 12*y[2] + 6*y[3] ]
* [ 0 1 4 1 ... 0 0 0 0 ] [ M[3] ] [ 6*y[2] - 12*y[3] + 6*y[4] ]
* [ 0 0 1 4 ... 0 0 0 0 ] [ M[4] ] [ 6*y[3] - 12*y[4] + 6*y[5] ]
* ... * = ...
* [ 0 0 0 0 ... 4 1 0 0 ] [ M[n-5] ] [ 6*y[n-6]- 12*y[n-5] + 6*y[n-4] ]
* [ 0 0 0 0 ... 1 4 1 0 ] [ M[n-4] ] [ 6*y[n-5]- 12*y[n-4] + 6*y[n-3] ]
* [ 0 0 0 0 ... 0 1 4 1 ] [ M[n-3] ] [ 6*y[n-4]- 12*y[n-3] + 6*y[n-2] ]
* [ 0 0 0 0 ... 0 0 1 4 ] [ M[n-2] ] [ 6*y[n-3]- 12*y[n-2] + 6*y[n-1] ]
*
* For our purpose we use natural splines which means the boundary coefficients
* M[0] = M[n-1] = 0
*
* The interpolation polynomial in the i-th interval then has the form
* p_i(x) = a3 (x - i)^3 + a2 (x - i)^2 + a1 (x - i) + a0
* = ((a3 * (x - i) + a2) * (x - i) + a1) * (x - i) + a0
* where
* a3 = (M[i+1] - M[i]) / 6
* a2 = M[i] / 2
* a1 = y[i+1] - y[i] - M[i+1]/6 - M[i]/3
* a0 = y[i]
*
* We solve the system by LU-factoring the matrix A:
* A = L * U:
*
* [ 4 1 0 0 ... 0 0 0 0 ] [ 1 0 0 0 ... 0 0 0 0 ] [ m[0] 1 0 0 ... 0 0 0 ]
* [ 1 4 1 0 ... 0 0 0 0 ] [ l[0] 1 0 0 ... 0 0 0 0 ] [ 0 m[1] 1 0 ... 0 0 0 ]
* [ 0 1 4 1 ... 0 0 0 0 ] [ 0 l[1] 1 0 ... 0 0 0 0 ] [ 0 0 m[2] 1 ... 0 0 0 ]
* [ 0 0 1 4 ... 0 0 0 0 ] [ 0 0 l[2] 1 ... 0 0 0 0 ] ...
* ... = ... * [ 0 0 0 0 ... 0 0 0 ]
* [ 0 0 0 0 ... 4 1 0 0 ] [ 0 0 0 0 ... 1 0 0 0 ] [ 0 0 0 0 ... 1 0 0 ]
* [ 0 0 0 0 ... 1 4 1 0 ] [ 0 0 0 0 ... l[n-6] 1 0 0 ] [ 0 0 0 0 ... m[n-5] 1 0 ]
* [ 0 0 0 0 ... 0 1 4 1 ] [ 0 0 0 0 ... 0 l[n-5] 1 0 ] [ 0 0 0 0 ... 0 m[n-4] 1 ]
* [ 0 0 0 0 ... 0 0 1 4 ] [ 0 0 0 0 ... 0 0 l[n-4] 1 ] [ 0 0 0 0 ... 0 0 m[n-3] ]
*
* where the l[i] and m[i] can be precomputed.
*
* Then we solve the system A * M = d by first solving the system
* L * t = d
* and then
* R * M = t
*/
class SplineInterpolation : public Interpolation {
protected:
double l[MAX_PERIOD_SIZE], m[MAX_PERIOD_SIZE];
public:
SplineInterpolation();
nframes_t interpolate (int channel, nframes_t nframes, Sample* input, Sample* output);
};
class LibSamplerateInterpolation : public Interpolation {
protected:
std::vector<SRC_STATE*> state;
std::vector<SRC_DATA*> data;
int error;
void reset_state ();
public:
LibSamplerateInterpolation ();
~LibSamplerateInterpolation ();
void set_speed (double new_speed);
void set_target_speed (double new_speed) {}
double speed () const { return _speed; }
void add_channel_to (int input_buffer_size, int output_buffer_size);
void remove_channel_from ();
nframes_t interpolate (int channel, nframes_t nframes, Sample* input, Sample* output);
void reset() { reset_state (); }
};
} // namespace ARDOUR
#endif
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