/*
* HRTF utility for producing and demonstrating the process of creating an
* OpenAL Soft compatible HRIR data set.
*
* Copyright (C) 2011-2019 Christopher Fitzgerald
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 2 of the License, or
* (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License along
* with this program; if not, write to the Free Software Foundation, Inc.,
* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
*
* Or visit: http://www.gnu.org/licenses/old-licenses/gpl-2.0.html
*
* --------------------------------------------------------------------------
*
* A big thanks goes out to all those whose work done in the field of
* binaural sound synthesis using measured HRTFs makes this utility and the
* OpenAL Soft implementation possible.
*
* The algorithm for diffuse-field equalization was adapted from the work
* done by Rio Emmanuel and Larcher Veronique of IRCAM and Bill Gardner of
* MIT Media Laboratory. It operates as follows:
*
* 1. Take the FFT of each HRIR and only keep the magnitude responses.
* 2. Calculate the diffuse-field power-average of all HRIRs weighted by
* their contribution to the total surface area covered by their
* measurement. This has since been modified to use coverage volume for
* multi-field HRIR data sets.
* 3. Take the diffuse-field average and limit its magnitude range.
* 4. Equalize the responses by using the inverse of the diffuse-field
* average.
* 5. Reconstruct the minimum-phase responses.
* 5. Zero the DC component.
* 6. IFFT the result and truncate to the desired-length minimum-phase FIR.
*
* The spherical head algorithm for calculating propagation delay was adapted
* from the paper:
*
* Modeling Interaural Time Difference Assuming a Spherical Head
* Joel David Miller
* Music 150, Musical Acoustics, Stanford University
* December 2, 2001
*
* The formulae for calculating the Kaiser window metrics are from the
* the textbook:
*
* Discrete-Time Signal Processing
* Alan V. Oppenheim and Ronald W. Schafer
* Prentice-Hall Signal Processing Series
* 1999
*/
#define _UNICODE
#include "config.h"
#include "makemhr.h"
#include <algorithm>
#include <atomic>
#include <chrono>
#include <cmath>
#include <complex>
#include <cstdint>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <functional>
#include <limits>
#include <memory>
#include <numeric>
#include <thread>
#include <utility>
#include <vector>
#ifdef HAVE_GETOPT
#include <unistd.h>
#else
#include "../getopt.h"
#endif
#include "loaddef.h"
#include "loadsofa.h"
#include "win_main_utf8.h"
namespace {
using namespace std::placeholders;
} // namespace
#ifndef M_PI
#define M_PI (3.14159265358979323846)
#endif
// Head model used for calculating the impulse delays.
enum HeadModelT {
HM_NONE,
HM_DATASET, // Measure the onset from the dataset.
HM_SPHERE // Calculate the onset using a spherical head model.
};
// The epsilon used to maintain signal stability.
#define EPSILON (1e-9)
// The limits to the FFT window size override on the command line.
#define MIN_FFTSIZE (65536)
#define MAX_FFTSIZE (131072)
// The limits to the equalization range limit on the command line.
#define MIN_LIMIT (2.0)
#define MAX_LIMIT (120.0)
// The limits to the truncation window size on the command line.
#define MIN_TRUNCSIZE (16)
#define MAX_TRUNCSIZE (512)
// The limits to the custom head radius on the command line.
#define MIN_CUSTOM_RADIUS (0.05)
#define MAX_CUSTOM_RADIUS (0.15)
// The truncation window size must be a multiple of the below value to allow
// for vectorized convolution.
#define MOD_TRUNCSIZE (8)
// The defaults for the command line options.
#define DEFAULT_FFTSIZE (65536)
#define DEFAULT_EQUALIZE (1)
#define DEFAULT_SURFACE (1)
#define DEFAULT_LIMIT (24.0)
#define DEFAULT_TRUNCSIZE (32)
#define DEFAULT_HEAD_MODEL (HM_DATASET)
#define DEFAULT_CUSTOM_RADIUS (0.0)
// The maximum propagation delay value supported by OpenAL Soft.
#define MAX_HRTD (63.0)
// The OpenAL Soft HRTF format marker. It stands for minimum-phase head
// response protocol 02.
#define MHR_FORMAT ("MinPHR02")
/* Channel index enums. Mono uses LeftChannel only. */
enum ChannelIndex : uint {
LeftChannel = 0u,
RightChannel = 1u
};
/* Performs a string substitution. Any case-insensitive occurrences of the
* pattern string are replaced with the replacement string. The result is
* truncated if necessary.
*/
static int StrSubst(const char *in, const char *pat, const char *rep, const size_t maxLen, char *out)
{
size_t inLen, patLen, repLen;
size_t si, di;
int truncated;
inLen = strlen(in);
patLen = strlen(pat);
repLen = strlen(rep);
si = 0;
di = 0;
truncated = 0;
while(si < inLen && di < maxLen)
{
if(patLen <= inLen-si)
{
if(strncasecmp(&in[si], pat, patLen) == 0)
{
if(repLen > maxLen-di)
{
repLen = maxLen - di;
truncated = 1;
}
strncpy(&out[di], rep, repLen);
si += patLen;
di += repLen;
}
}
out[di] = in[si];
si++;
di++;
}
if(si < inLen)
truncated = 1;
out[di] = '\0';
return !truncated;
}
/*********************
*** Math routines ***
*********************/
// Simple clamp routine.
static double Clamp(const double val, const double lower, const double upper)
{
return std::min(std::max(val, lower), upper);
}
static inline uint dither_rng(uint *seed)
{
*seed = *seed * 96314165 + 907633515;
return *seed;
}
// Performs a triangular probability density function dither. The input samples
// should be normalized (-1 to +1).
static void TpdfDither(double *RESTRICT out, const double *RESTRICT in, const double scale,
const uint count, const uint step, uint *seed)
{
static constexpr double PRNG_SCALE = 1.0 / std::numeric_limits<uint>::max();
for(uint i{0};i < count;i++)
{
uint prn0{dither_rng(seed)};
uint prn1{dither_rng(seed)};
*out = std::round(*(in++)*scale + (prn0*PRNG_SCALE - prn1*PRNG_SCALE));
out += step;
}
}
/* Fast Fourier transform routines. The number of points must be a power of
* two.
*/
// Performs bit-reversal ordering.
static void FftArrange(const uint n, complex_d *inout)
{
// Handle in-place arrangement.
uint rk{0u};
for(uint k{0u};k < n;k++)
{
if(rk > k)
std::swap(inout[rk], inout[k]);
uint m{n};
while(rk&(m >>= 1))
rk &= ~m;
rk |= m;
}
}
// Performs the summation.
static void FftSummation(const uint n, const double s, complex_d *cplx)
{
double pi;
uint m, m2;
uint i, k, mk;
pi = s * M_PI;
for(m = 1, m2 = 2;m < n; m <<= 1, m2 <<= 1)
{
// v = Complex (-2.0 * sin (0.5 * pi / m) * sin (0.5 * pi / m), -sin (pi / m))
double sm = std::sin(0.5 * pi / m);
auto v = complex_d{-2.0*sm*sm, -std::sin(pi / m)};
auto w = complex_d{1.0, 0.0};
for(i = 0;i < m;i++)
{
for(k = i;k < n;k += m2)
{
mk = k + m;
auto t = w * cplx[mk];
cplx[mk] = cplx[k] - t;
cplx[k] = cplx[k] + t;
}
w += v*w;
}
}
}
// Performs a forward FFT.
void FftForward(const uint n, complex_d *inout)
{
FftArrange(n, inout);
FftSummation(n, 1.0, inout);
}
// Performs an inverse FFT.
void FftInverse(const uint n, complex_d *inout)
{
FftArrange(n, inout);
FftSummation(n, -1.0, inout);
double f{1.0 / n};
for(uint i{0};i < n;i++)
inout[i] *= f;
}
/* Calculate the complex helical sequence (or discrete-time analytical signal)
* of the given input using the Hilbert transform. Given the natural logarithm
* of a signal's magnitude response, the imaginary components can be used as
* the angles for minimum-phase reconstruction.
*/
static void Hilbert(const uint n, complex_d *inout)
{
uint i;
// Handle in-place operation.
for(i = 0;i < n;i++)
inout[i].imag(0.0);
FftInverse(n, inout);
for(i = 1;i < (n+1)/2;i++)
inout[i] *= 2.0;
/* Increment i if n is even. */
i += (n&1)^1;
for(;i < n;i++)
inout[i] = complex_d{0.0, 0.0};
FftForward(n, inout);
}
/* Calculate the magnitude response of the given input. This is used in
* place of phase decomposition, since the phase residuals are discarded for
* minimum phase reconstruction. The mirrored half of the response is also
* discarded.
*/
void MagnitudeResponse(const uint n, const complex_d *in, double *out)
{
const uint m = 1 + (n / 2);
uint i;
for(i = 0;i < m;i++)
out[i] = std::max(std::abs(in[i]), EPSILON);
}
/* Apply a range limit (in dB) to the given magnitude response. This is used
* to adjust the effects of the diffuse-field average on the equalization
* process.
*/
static void LimitMagnitudeResponse(const uint n, const uint m, const double limit, const double *in, double *out)
{
double halfLim;
uint i, lower, upper;
double ave;
halfLim = limit / 2.0;
// Convert the response to dB.
for(i = 0;i < m;i++)
out[i] = 20.0 * std::log10(in[i]);
// Use six octaves to calculate the average magnitude of the signal.
lower = (static_cast<uint>(std::ceil(n / std::pow(2.0, 8.0)))) - 1;
upper = (static_cast<uint>(std::floor(n / std::pow(2.0, 2.0)))) - 1;
ave = 0.0;
for(i = lower;i <= upper;i++)
ave += out[i];
ave /= upper - lower + 1;
// Keep the response within range of the average magnitude.
for(i = 0;i < m;i++)
out[i] = Clamp(out[i], ave - halfLim, ave + halfLim);
// Convert the response back to linear magnitude.
for(i = 0;i < m;i++)
out[i] = std::pow(10.0, out[i] / 20.0);
}
/* Reconstructs the minimum-phase component for the given magnitude response
* of a signal. This is equivalent to phase recomposition, sans the missing
* residuals (which were discarded). The mirrored half of the response is
* reconstructed.
*/
static void MinimumPhase(const uint n, const double *in, complex_d *out)
{
const uint m = 1 + (n / 2);
std::vector<double> mags(n);
uint i;
for(i = 0;i < m;i++)
{
mags[i] = std::max(EPSILON, in[i]);
out[i] = complex_d{std::log(mags[i]), 0.0};
}
for(;i < n;i++)
{
mags[i] = mags[n - i];
out[i] = out[n - i];
}
Hilbert(n, out);
// Remove any DC offset the filter has.
mags[0] = EPSILON;
for(i = 0;i < n;i++)
{
auto a = std::exp(complex_d{0.0, out[i].imag()});
out[i] = complex_d{mags[i], 0.0} * a;
}
}
/***************************
*** Resampler functions ***
***************************/
/* This is the normalized cardinal sine (sinc) function.
*
* sinc(x) = { 1, x = 0
* { sin(pi x) / (pi x), otherwise.
*/
static double Sinc(const double x)
{
if(std::abs(x) < EPSILON)
return 1.0;
return std::sin(M_PI * x) / (M_PI * x);
}
/* The zero-order modified Bessel function of the first kind, used for the
* Kaiser window.
*
* I_0(x) = sum_{k=0}^inf (1 / k!)^2 (x / 2)^(2 k)
* = sum_{k=0}^inf ((x / 2)^k / k!)^2
*/
static double BesselI_0(const double x)
{
double term, sum, x2, y, last_sum;
int k;
// Start at k=1 since k=0 is trivial.
term = 1.0;
sum = 1.0;
x2 = x/2.0;
k = 1;
// Let the integration converge until the term of the sum is no longer
// significant.
do {
y = x2 / k;
k++;
last_sum = sum;
term *= y * y;
sum += term;
} while(sum != last_sum);
return sum;
}
/* Calculate a Kaiser window from the given beta value and a normalized k
* [-1, 1].
*
* w(k) = { I_0(B sqrt(1 - k^2)) / I_0(B), -1 <= k <= 1
* { 0, elsewhere.
*
* Where k can be calculated as:
*
* k = i / l, where -l <= i <= l.
*
* or:
*
* k = 2 i / M - 1, where 0 <= i <= M.
*/
static double Kaiser(const double b, const double k)
{
if(!(k >= -1.0 && k <= 1.0))
return 0.0;
return BesselI_0(b * std::sqrt(1.0 - k*k)) / BesselI_0(b);
}
// Calculates the greatest common divisor of a and b.
static uint Gcd(uint x, uint y)
{
while(y > 0)
{
uint z{y};
y = x % y;
x = z;
}
return x;
}
/* Calculates the size (order) of the Kaiser window. Rejection is in dB and
* the transition width is normalized frequency (0.5 is nyquist).
*
* M = { ceil((r - 7.95) / (2.285 2 pi f_t)), r > 21
* { ceil(5.79 / 2 pi f_t), r <= 21.
*
*/
static uint CalcKaiserOrder(const double rejection, const double transition)
{
double w_t = 2.0 * M_PI * transition;
if(rejection > 21.0)
return static_cast<uint>(std::ceil((rejection - 7.95) / (2.285 * w_t)));
return static_cast<uint>(std::ceil(5.79 / w_t));
}
// Calculates the beta value of the Kaiser window. Rejection is in dB.
static double CalcKaiserBeta(const double rejection)
{
if(rejection > 50.0)
return 0.1102 * (rejection - 8.7);
if(rejection >= 21.0)
return (0.5842 * std::pow(rejection - 21.0, 0.4)) +
(0.07886 * (rejection - 21.0));
return 0.0;
}
/* Calculates a point on the Kaiser-windowed sinc filter for the given half-
* width, beta, gain, and cutoff. The point is specified in non-normalized
* samples, from 0 to M, where M = (2 l + 1).
*
* w(k) 2 p f_t sinc(2 f_t x)
*
* x -- centered sample index (i - l)
* k -- normalized and centered window index (x / l)
* w(k) -- window function (Kaiser)
* p -- gain compensation factor when sampling
* f_t -- normalized center frequency (or cutoff; 0.5 is nyquist)
*/
static double SincFilter(const uint l, const double b, const double gain, const double cutoff, const uint i)
{
return Kaiser(b, static_cast<double>(i - l) / l) * 2.0 * gain * cutoff * Sinc(2.0 * cutoff * (i - l));
}
/* This is a polyphase sinc-filtered resampler.
*
* Upsample Downsample
*
* p/q = 3/2 p/q = 3/5
*
* M-+-+-+-> M-+-+-+->
* -------------------+ ---------------------+
* p s * f f f f|f| | p s * f f f f f |
* | 0 * 0 0 0|0|0 | | 0 * 0 0 0 0|0| |
* v 0 * 0 0|0|0 0 | v 0 * 0 0 0|0|0 |
* s * f|f|f f f | s * f f|f|f f |
* 0 * |0|0 0 0 0 | 0 * 0|0|0 0 0 |
* --------+=+--------+ 0 * |0|0 0 0 0 |
* d . d .|d|. d . d ----------+=+--------+
* d . . . .|d|. . . .
* q->
* q-+-+-+->
*
* P_f(i,j) = q i mod p + pj
* P_s(i,j) = floor(q i / p) - j
* d[i=0..N-1] = sum_{j=0}^{floor((M - 1) / p)} {
* { f[P_f(i,j)] s[P_s(i,j)], P_f(i,j) < M
* { 0, P_f(i,j) >= M. }
*/
// Calculate the resampling metrics and build the Kaiser-windowed sinc filter
// that's used to cut frequencies above the destination nyquist.
void ResamplerSetup(ResamplerT *rs, const uint srcRate, const uint dstRate)
{
const uint gcd{Gcd(srcRate, dstRate)};
rs->mP = dstRate / gcd;
rs->mQ = srcRate / gcd;
/* The cutoff is adjusted by half the transition width, so the transition
* ends before the nyquist (0.5). Both are scaled by the downsampling
* factor.
*/
double cutoff, width;
if(rs->mP > rs->mQ)
{
cutoff = 0.475 / rs->mP;
width = 0.05 / rs->mP;
}
else
{
cutoff = 0.475 / rs->mQ;
width = 0.05 / rs->mQ;
}
// A rejection of -180 dB is used for the stop band. Round up when
// calculating the left offset to avoid increasing the transition width.
const uint l{(CalcKaiserOrder(180.0, width)+1) / 2};
const double beta{CalcKaiserBeta(180.0)};
rs->mM = l*2 + 1;
rs->mL = l;
rs->mF.resize(rs->mM);
for(uint i{0};i < rs->mM;i++)
rs->mF[i] = SincFilter(l, beta, rs->mP, cutoff, i);
}
// Perform the upsample-filter-downsample resampling operation using a
// polyphase filter implementation.
void ResamplerRun(ResamplerT *rs, const uint inN, const double *in, const uint outN, double *out)
{
const uint p = rs->mP, q = rs->mQ, m = rs->mM, l = rs->mL;
std::vector<double> workspace;
const double *f = rs->mF.data();
uint j_f, j_s;
double *work;
uint i;
if(outN == 0)
return;
// Handle in-place operation.
if(in == out)
{
workspace.resize(outN);
work = workspace.data();
}
else
work = out;
// Resample the input.
for(i = 0;i < outN;i++)
{
double r = 0.0;
// Input starts at l to compensate for the filter delay. This will
// drop any build-up from the first half of the filter.
j_f = (l + (q * i)) % p;
j_s = (l + (q * i)) / p;
while(j_f < m)
{
// Only take input when 0 <= j_s < inN. This single unsigned
// comparison catches both cases.
if(j_s < inN)
r += f[j_f] * in[j_s];
j_f += p;
j_s--;
}
work[i] = r;
}
// Clean up after in-place operation.
if(work != out)
{
for(i = 0;i < outN;i++)
out[i] = work[i];
}
}
/***************************
*** File storage output ***
***************************/
// Write an ASCII string to a file.
static int WriteAscii(const char *out, FILE *fp, const char *filename)
{
size_t len;
len = strlen(out);
if(fwrite(out, 1, len, fp) != len)
{
fclose(fp);
fprintf(stderr, "\nError: Bad write to file '%s'.\n", filename);
return 0;
}
return 1;
}
// Write a binary value of the given byte order and byte size to a file,
// loading it from a 32-bit unsigned integer.
static int WriteBin4(const uint bytes, const uint32_t in, FILE *fp, const char *filename)
{
uint8_t out[4];
uint i;
for(i = 0;i < bytes;i++)
out[i] = (in>>(i*8)) & 0x000000FF;
if(fwrite(out, 1, bytes, fp) != bytes)
{
fprintf(stderr, "\nError: Bad write to file '%s'.\n", filename);
return 0;
}
return 1;
}
// Store the OpenAL Soft HRTF data set.
static int StoreMhr(const HrirDataT *hData, const char *filename)
{
uint channels = (hData->mChannelType == CT_STEREO) ? 2 : 1;
uint n = hData->mIrPoints;
FILE *fp;
uint fi, ei, ai, i;
uint dither_seed = 22222;
if((fp=fopen(filename, "wb")) == nullptr)
{
fprintf(stderr, "\nError: Could not open MHR file '%s'.\n", filename);
return 0;
}
if(!WriteAscii(MHR_FORMAT, fp, filename))
return 0;
if(!WriteBin4(4, hData->mIrRate, fp, filename))
return 0;
if(!WriteBin4(1, static_cast<uint32_t>(hData->mSampleType), fp, filename))
return 0;
if(!WriteBin4(1, static_cast<uint32_t>(hData->mChannelType), fp, filename))
return 0;
if(!WriteBin4(1, hData->mIrPoints, fp, filename))
return 0;
if(!WriteBin4(1, hData->mFdCount, fp, filename))
return 0;
for(fi = 0;fi < hData->mFdCount;fi++)
{
auto fdist = static_cast<uint32_t>(std::round(1000.0 * hData->mFds[fi].mDistance));
if(!WriteBin4(2, fdist, fp, filename))
return 0;
if(!WriteBin4(1, hData->mFds[fi].mEvCount, fp, filename))
return 0;
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
if(!WriteBin4(1, hData->mFds[fi].mEvs[ei].mAzCount, fp, filename))
return 0;
}
}
for(fi = 0;fi < hData->mFdCount;fi++)
{
const double scale = (hData->mSampleType == ST_S16) ? 32767.0 :
((hData->mSampleType == ST_S24) ? 8388607.0 : 0.0);
const uint bps = (hData->mSampleType == ST_S16) ? 2 :
((hData->mSampleType == ST_S24) ? 3 : 0);
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
double out[2 * MAX_TRUNCSIZE];
TpdfDither(out, azd->mIrs[0], scale, n, channels, &dither_seed);
if(hData->mChannelType == CT_STEREO)
TpdfDither(out+1, azd->mIrs[1], scale, n, channels, &dither_seed);
for(i = 0;i < (channels * n);i++)
{
int v = static_cast<int>(Clamp(out[i], -scale-1.0, scale));
if(!WriteBin4(bps, static_cast<uint32_t>(v), fp, filename))
return 0;
}
}
}
}
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
const HrirAzT &azd = hData->mFds[fi].mEvs[ei].mAzs[ai];
int v = static_cast<int>(std::min(std::round(hData->mIrRate * azd.mDelays[0]), MAX_HRTD));
if(!WriteBin4(1, static_cast<uint32_t>(v), fp, filename))
return 0;
if(hData->mChannelType == CT_STEREO)
{
v = static_cast<int>(std::min(std::round(hData->mIrRate * azd.mDelays[1]), MAX_HRTD));
if(!WriteBin4(1, static_cast<uint32_t>(v), fp, filename))
return 0;
}
}
}
}
fclose(fp);
return 1;
}
/***********************
*** HRTF processing ***
***********************/
/* Balances the maximum HRIR magnitudes of multi-field data sets by
* independently normalizing each field in relation to the overall maximum.
* This is done to ignore distance attenuation.
*/
static void BalanceFieldMagnitudes(const HrirDataT *hData, const uint channels, const uint m)
{
double maxMags[MAX_FD_COUNT];
uint fi, ei, ai, ti, i;
double maxMag{0.0};
for(fi = 0;fi < hData->mFdCount;fi++)
{
maxMags[fi] = 0.0;
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
for(ti = 0;ti < channels;ti++)
{
for(i = 0;i < m;i++)
maxMags[fi] = std::max(azd->mIrs[ti][i], maxMags[fi]);
}
}
}
maxMag = std::max(maxMags[fi], maxMag);
}
for(fi = 0;fi < hData->mFdCount;fi++)
{
const double magFactor{maxMag / maxMags[fi]};
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
for(ti = 0;ti < channels;ti++)
{
for(i = 0;i < m;i++)
azd->mIrs[ti][i] *= magFactor;
}
}
}
}
}
/* Calculate the contribution of each HRIR to the diffuse-field average based
* on its coverage volume. All volumes are centered at the spherical HRIR
* coordinates and measured by extruded solid angle.
*/
static void CalculateDfWeights(const HrirDataT *hData, double *weights)
{
double sum, innerRa, outerRa, evs, ev, upperEv, lowerEv;
double solidAngle, solidVolume;
uint fi, ei;
sum = 0.0;
// The head radius acts as the limit for the inner radius.
innerRa = hData->mRadius;
for(fi = 0;fi < hData->mFdCount;fi++)
{
// Each volume ends half way between progressive field measurements.
if((fi + 1) < hData->mFdCount)
outerRa = 0.5f * (hData->mFds[fi].mDistance + hData->mFds[fi + 1].mDistance);
// The final volume has its limit extended to some practical value.
// This is done to emphasize the far-field responses in the average.
else
outerRa = 10.0f;
evs = M_PI / 2.0 / (hData->mFds[fi].mEvCount - 1);
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
// For each elevation, calculate the upper and lower limits of
// the patch band.
ev = hData->mFds[fi].mEvs[ei].mElevation;
lowerEv = std::max(-M_PI / 2.0, ev - evs);
upperEv = std::min(M_PI / 2.0, ev + evs);
// Calculate the surface area of the patch band.
solidAngle = 2.0 * M_PI * (std::sin(upperEv) - std::sin(lowerEv));
// Then the volume of the extruded patch band.
solidVolume = solidAngle * (std::pow(outerRa, 3.0) - std::pow(innerRa, 3.0)) / 3.0;
// Each weight is the volume of one extruded patch.
weights[(fi * MAX_EV_COUNT) + ei] = solidVolume / hData->mFds[fi].mEvs[ei].mAzCount;
// Sum the total coverage volume of the HRIRs for all fields.
sum += solidAngle;
}
innerRa = outerRa;
}
for(fi = 0;fi < hData->mFdCount;fi++)
{
// Normalize the weights given the total surface coverage for all
// fields.
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
weights[(fi * MAX_EV_COUNT) + ei] /= sum;
}
}
/* Calculate the diffuse-field average from the given magnitude responses of
* the HRIR set. Weighting can be applied to compensate for the varying
* coverage of each HRIR. The final average can then be limited by the
* specified magnitude range (in positive dB; 0.0 to skip).
*/
static void CalculateDiffuseFieldAverage(const HrirDataT *hData, const uint channels, const uint m, const int weighted, const double limit, double *dfa)
{
std::vector<double> weights(hData->mFdCount * MAX_EV_COUNT);
uint count, ti, fi, ei, i, ai;
if(weighted)
{
// Use coverage weighting to calculate the average.
CalculateDfWeights(hData, weights.data());
}
else
{
double weight;
// If coverage weighting is not used, the weights still need to be
// averaged by the number of existing HRIRs.
count = hData->mIrCount;
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = 0;ei < hData->mFds[fi].mEvStart;ei++)
count -= hData->mFds[fi].mEvs[ei].mAzCount;
}
weight = 1.0 / count;
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
weights[(fi * MAX_EV_COUNT) + ei] = weight;
}
}
for(ti = 0;ti < channels;ti++)
{
for(i = 0;i < m;i++)
dfa[(ti * m) + i] = 0.0;
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
// Get the weight for this HRIR's contribution.
double weight = weights[(fi * MAX_EV_COUNT) + ei];
// Add this HRIR's weighted power average to the total.
for(i = 0;i < m;i++)
dfa[(ti * m) + i] += weight * azd->mIrs[ti][i] * azd->mIrs[ti][i];
}
}
}
// Finish the average calculation and keep it from being too small.
for(i = 0;i < m;i++)
dfa[(ti * m) + i] = std::max(sqrt(dfa[(ti * m) + i]), EPSILON);
// Apply a limit to the magnitude range of the diffuse-field average
// if desired.
if(limit > 0.0)
LimitMagnitudeResponse(hData->mFftSize, m, limit, &dfa[ti * m], &dfa[ti * m]);
}
}
// Perform diffuse-field equalization on the magnitude responses of the HRIR
// set using the given average response.
static void DiffuseFieldEqualize(const uint channels, const uint m, const double *dfa, const HrirDataT *hData)
{
uint ti, fi, ei, ai, i;
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
for(ti = 0;ti < channels;ti++)
{
for(i = 0;i < m;i++)
azd->mIrs[ti][i] /= dfa[(ti * m) + i];
}
}
}
}
}
/* Perform minimum-phase reconstruction using the magnitude responses of the
* HRIR set. Work is delegated to this struct, which runs asynchronously on one
* or more threads (sharing the same reconstructor object).
*/
struct HrirReconstructor {
std::vector<double*> mIrs;
std::atomic<size_t> mCurrent;
std::atomic<size_t> mDone;
uint mFftSize;
uint mIrPoints;
void Worker()
{
auto h = std::vector<complex_d>(mFftSize);
while(1)
{
/* Load the current index to process. */
size_t idx{mCurrent.load()};
do {
/* If the index is at the end, we're done. */
if(idx >= mIrs.size())
return;
/* Otherwise, increment the current index atomically so other
* threads know to go to the next one. If this call fails, the
* current index was just changed by another thread and the new
* value is loaded into idx, which we'll recheck.
*/
} while(!mCurrent.compare_exchange_weak(idx, idx+1, std::memory_order_relaxed));
/* Now do the reconstruction, and apply the inverse FFT to get the
* time-domain response.
*/
MinimumPhase(mFftSize, mIrs[idx], h.data());
FftInverse(mFftSize, h.data());
for(uint i{0u};i < mIrPoints;++i)
mIrs[idx][i] = h[i].real();
/* Increment the number of IRs done. */
mDone.fetch_add(1);
}
}
};
static void ReconstructHrirs(const HrirDataT *hData)
{
const uint channels{(hData->mChannelType == CT_STEREO) ? 2u : 1u};
/* Count the number of IRs to process (excluding elevations that will be
* synthesized later).
*/
size_t total{hData->mIrCount};
for(uint fi{0u};fi < hData->mFdCount;fi++)
{
for(uint ei{0u};ei < hData->mFds[fi].mEvStart;ei++)
total -= hData->mFds[fi].mEvs[ei].mAzCount;
}
total *= channels;
/* Set up the reconstructor with the needed size info and pointers to the
* IRs to process.
*/
HrirReconstructor reconstructor;
reconstructor.mIrs.reserve(total);
reconstructor.mCurrent.store(0, std::memory_order_relaxed);
reconstructor.mDone.store(0, std::memory_order_relaxed);
reconstructor.mFftSize = hData->mFftSize;
reconstructor.mIrPoints = hData->mIrPoints;
for(uint fi{0u};fi < hData->mFdCount;fi++)
{
const HrirFdT &field = hData->mFds[fi];
for(uint ei{field.mEvStart};ei < field.mEvCount;ei++)
{
const HrirEvT &elev = field.mEvs[ei];
for(uint ai{0u};ai < elev.mAzCount;ai++)
{
const HrirAzT &azd = elev.mAzs[ai];
for(uint ti{0u};ti < channels;ti++)
reconstructor.mIrs.push_back(azd.mIrs[ti]);
}
}
}
/* Launch two threads to work on reconstruction. */
std::thread thrd1{std::mem_fn(&HrirReconstructor::Worker), &reconstructor};
std::thread thrd2{std::mem_fn(&HrirReconstructor::Worker), &reconstructor};
/* Keep track of the number of IRs done, periodically reporting it. */
size_t count;
while((count=reconstructor.mDone.load()) != total)
{
size_t pcdone{count * 100 / total};
printf("\r%3zu%% done (%zu of %zu)", pcdone, count, total);
fflush(stdout);
std::this_thread::sleep_for(std::chrono::milliseconds{50});
}
size_t pcdone{count * 100 / total};
printf("\r%3zu%% done (%zu of %zu)\n", pcdone, count, total);
if(thrd2.joinable()) thrd2.join();
if(thrd1.joinable()) thrd1.join();
}
// Resamples the HRIRs for use at the given sampling rate.
static void ResampleHrirs(const uint rate, HrirDataT *hData)
{
uint channels = (hData->mChannelType == CT_STEREO) ? 2 : 1;
uint n = hData->mIrPoints;
uint ti, fi, ei, ai;
ResamplerT rs;
ResamplerSetup(&rs, hData->mIrRate, rate);
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = hData->mFds[fi].mEvStart;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
for(ti = 0;ti < channels;ti++)
ResamplerRun(&rs, n, azd->mIrs[ti], n, azd->mIrs[ti]);
}
}
}
hData->mIrRate = rate;
}
/* Given field and elevation indices and an azimuth, calculate the indices of
* the two HRIRs that bound the coordinate along with a factor for
* calculating the continuous HRIR using interpolation.
*/
static void CalcAzIndices(const HrirFdT &field, const uint ei, const double az, uint *a0, uint *a1, double *af)
{
double f{(2.0*M_PI + az) * field.mEvs[ei].mAzCount / (2.0*M_PI)};
uint i{static_cast<uint>(f) % field.mEvs[ei].mAzCount};
f -= std::floor(f);
*a0 = i;
*a1 = (i + 1) % field.mEvs[ei].mAzCount;
*af = f;
}
/* Synthesize any missing onset timings at the bottom elevations of each field.
* This just mirrors some top elevations for the bottom, and blends the
* remaining elevations (not an accurate model).
*/
static void SynthesizeOnsets(HrirDataT *hData)
{
const uint channels{(hData->mChannelType == CT_STEREO) ? 2u : 1u};
auto proc_field = [channels](HrirFdT &field) -> void
{
/* Get the starting elevation from the measurements, and use it as the
* upper elevation limit for what needs to be calculated.
*/
const uint upperElevReal{field.mEvStart};
if(upperElevReal <= 0) return;
/* Get the lowest half of the missing elevations' delays by mirroring
* the top elevation delays. The responses are on a spherical grid
* centered between the ears, so these should align.
*/
uint ei{};
if(channels > 1)
{
/* Take the polar opposite position of the desired measurement and
* swap the ears.
*/
field.mEvs[0].mAzs[0].mDelays[0] = field.mEvs[field.mEvCount-1].mAzs[0].mDelays[1];
field.mEvs[0].mAzs[0].mDelays[1] = field.mEvs[field.mEvCount-1].mAzs[0].mDelays[0];
for(ei = 1u;ei < (upperElevReal+1)/2;++ei)
{
const uint topElev{field.mEvCount-ei-1};
for(uint ai{0u};ai < field.mEvs[ei].mAzCount;ai++)
{
uint a0, a1;
double af;
/* Rotate this current azimuth by a half-circle, and lookup
* the mirrored elevation to find the indices for the polar
* opposite position (may need blending).
*/
const double az{field.mEvs[ei].mAzs[ai].mAzimuth + M_PI};
CalcAzIndices(field, topElev, az, &a0, &a1, &af);
/* Blend the delays, and again, swap the ears. */
field.mEvs[ei].mAzs[ai].mDelays[0] = Lerp(
field.mEvs[topElev].mAzs[a0].mDelays[1],
field.mEvs[topElev].mAzs[a1].mDelays[1], af);
field.mEvs[ei].mAzs[ai].mDelays[1] = Lerp(
field.mEvs[topElev].mAzs[a0].mDelays[0],
field.mEvs[topElev].mAzs[a1].mDelays[0], af);
}
}
}
else
{
field.mEvs[0].mAzs[0].mDelays[0] = field.mEvs[field.mEvCount-1].mAzs[0].mDelays[0];
for(ei = 1u;ei < (upperElevReal+1)/2;++ei)
{
const uint topElev{field.mEvCount-ei-1};
for(uint ai{0u};ai < field.mEvs[ei].mAzCount;ai++)
{
uint a0, a1;
double af;
/* For mono data sets, mirror the azimuth front<->back
* since the other ear is a mirror of what we have (e.g.
* the left ear's back-left is simulated with the right
* ear's front-right, which uses the left ear's front-left
* measurement).
*/
double az{field.mEvs[ei].mAzs[ai].mAzimuth};
if(az <= M_PI) az = M_PI - az;
else az = (M_PI*2.0)-az + M_PI;
CalcAzIndices(field, topElev, az, &a0, &a1, &af);
field.mEvs[ei].mAzs[ai].mDelays[0] = Lerp(
field.mEvs[topElev].mAzs[a0].mDelays[0],
field.mEvs[topElev].mAzs[a1].mDelays[0], af);
}
}
}
/* Record the lowest elevation filled in with the mirrored top. */
const uint lowerElevFake{ei-1u};
/* Fill in the remaining delays using bilinear interpolation. This
* helps smooth the transition back to the real delays.
*/
for(;ei < upperElevReal;++ei)
{
const double ef{(field.mEvs[upperElevReal].mElevation - field.mEvs[ei].mElevation) /
(field.mEvs[upperElevReal].mElevation - field.mEvs[lowerElevFake].mElevation)};
for(uint ai{0u};ai < field.mEvs[ei].mAzCount;ai++)
{
uint a0, a1, a2, a3;
double af0, af1;
double az{field.mEvs[ei].mAzs[ai].mAzimuth};
CalcAzIndices(field, upperElevReal, az, &a0, &a1, &af0);
CalcAzIndices(field, lowerElevFake, az, &a2, &a3, &af1);
double blend[4]{
(1.0-ef) * (1.0-af0),
(1.0-ef) * ( af0),
( ef) * (1.0-af1),
( ef) * ( af1)
};
for(uint ti{0u};ti < channels;ti++)
{
field.mEvs[ei].mAzs[ai].mDelays[ti] =
field.mEvs[upperElevReal].mAzs[a0].mDelays[ti]*blend[0] +
field.mEvs[upperElevReal].mAzs[a1].mDelays[ti]*blend[1] +
field.mEvs[lowerElevFake].mAzs[a2].mDelays[ti]*blend[2] +
field.mEvs[lowerElevFake].mAzs[a3].mDelays[ti]*blend[3];
}
}
}
};
std::for_each(hData->mFds.begin(), hData->mFds.begin()+hData->mFdCount, proc_field);
}
/* Attempt to synthesize any missing HRIRs at the bottom elevations of each
* field. Right now this just blends the lowest elevation HRIRs together and
* applies some attenuation and high frequency damping. It is a simple, if
* inaccurate model.
*/
static void SynthesizeHrirs(HrirDataT *hData)
{
const uint channels{(hData->mChannelType == CT_STEREO) ? 2u : 1u};
const uint irSize{hData->mIrPoints};
const double beta{3.5e-6 * hData->mIrRate};
auto proc_field = [channels,irSize,beta](HrirFdT &field) -> void
{
const uint oi{field.mEvStart};
if(oi <= 0) return;
for(uint ti{0u};ti < channels;ti++)
{
for(uint i{0u};i < irSize;i++)
field.mEvs[0].mAzs[0].mIrs[ti][i] = 0.0;
/* Blend the lowest defined elevation's responses for an average
* -90 degree elevation response.
*/
double blend_count{0.0};
for(uint ai{0u};ai < field.mEvs[oi].mAzCount;ai++)
{
/* Only include the left responses for the left ear, and the
* right responses for the right ear. This removes the cross-
* talk that shouldn't exist for the -90 degree elevation
* response (and would be mistimed anyway). NOTE: Azimuth goes
* from 0...2pi rather than -pi...+pi (0 in front, clockwise).
*/
if(std::abs(field.mEvs[oi].mAzs[ai].mAzimuth) < EPSILON ||
(ti == LeftChannel && field.mEvs[oi].mAzs[ai].mAzimuth > M_PI-EPSILON) ||
(ti == RightChannel && field.mEvs[oi].mAzs[ai].mAzimuth < M_PI+EPSILON))
{
for(uint i{0u};i < irSize;i++)
field.mEvs[0].mAzs[0].mIrs[ti][i] += field.mEvs[oi].mAzs[ai].mIrs[ti][i];
blend_count += 1.0;
}
}
if(blend_count > 0.0)
{
for(uint i{0u};i < irSize;i++)
field.mEvs[0].mAzs[0].mIrs[ti][i] /= blend_count;
}
for(uint ei{1u};ei < field.mEvStart;ei++)
{
const double of{static_cast<double>(ei) / field.mEvStart};
const double b{(1.0 - of) * beta};
for(uint ai{0u};ai < field.mEvs[ei].mAzCount;ai++)
{
uint a0, a1;
double af;
CalcAzIndices(field, oi, field.mEvs[ei].mAzs[ai].mAzimuth, &a0, &a1, &af);
double lp[4]{};
for(uint i{0u};i < irSize;i++)
{
/* Blend the two defined HRIRs closest to this azimuth,
* then blend that with the synthesized -90 elevation.
*/
const double s1{Lerp(field.mEvs[oi].mAzs[a0].mIrs[ti][i],
field.mEvs[oi].mAzs[a1].mIrs[ti][i], af)};
const double s0{Lerp(field.mEvs[0].mAzs[0].mIrs[ti][i], s1, of)};
/* Apply a low-pass to simulate body occlusion. */
lp[0] = Lerp(s0, lp[0], b);
lp[1] = Lerp(lp[0], lp[1], b);
lp[2] = Lerp(lp[1], lp[2], b);
lp[3] = Lerp(lp[2], lp[3], b);
field.mEvs[ei].mAzs[ai].mIrs[ti][i] = lp[3];
}
}
}
const double b{beta};
double lp[4]{};
for(uint i{0u};i < irSize;i++)
{
const double s0{field.mEvs[0].mAzs[0].mIrs[ti][i]};
lp[0] = Lerp(s0, lp[0], b);
lp[1] = Lerp(lp[0], lp[1], b);
lp[2] = Lerp(lp[1], lp[2], b);
lp[3] = Lerp(lp[2], lp[3], b);
field.mEvs[0].mAzs[0].mIrs[ti][i] = lp[3];
}
}
field.mEvStart = 0;
};
std::for_each(hData->mFds.begin(), hData->mFds.begin()+hData->mFdCount, proc_field);
}
// The following routines assume a full set of HRIRs for all elevations.
// Normalize the HRIR set and slightly attenuate the result.
static void NormalizeHrirs(HrirDataT *hData)
{
const uint channels{(hData->mChannelType == CT_STEREO) ? 2u : 1u};
const uint irSize{hData->mIrPoints};
/* Find the maximum amplitude and RMS out of all the IRs. */
struct LevelPair { double amp, rms; };
auto proc0_field = [channels,irSize](const LevelPair levels, const HrirFdT &field) -> LevelPair
{
auto proc_elev = [channels,irSize](const LevelPair levels, const HrirEvT &elev) -> LevelPair
{
auto proc_azi = [channels,irSize](const LevelPair levels, const HrirAzT &azi) -> LevelPair
{
auto proc_channel = [irSize](const LevelPair levels, const double *ir) -> LevelPair
{
/* Calculate the peak amplitude and RMS of this IR. */
auto current = std::accumulate(ir, ir+irSize, LevelPair{0.0, 0.0},
[](const LevelPair current, const double impulse) -> LevelPair
{
return LevelPair{std::max(std::abs(impulse), current.amp),
current.rms + impulse*impulse};
});
current.rms = std::sqrt(current.rms / irSize);
/* Accumulate levels by taking the maximum amplitude and RMS. */
return LevelPair{std::max(current.amp, levels.amp),
std::max(current.rms, levels.rms)};
};
return std::accumulate(azi.mIrs, azi.mIrs+channels, levels, proc_channel);
};
return std::accumulate(elev.mAzs, elev.mAzs+elev.mAzCount, levels, proc_azi);
};
return std::accumulate(field.mEvs, field.mEvs+field.mEvCount, levels, proc_elev);
};
const auto maxlev = std::accumulate(hData->mFds.begin(), hData->mFds.begin()+hData->mFdCount,
LevelPair{0.0, 0.0}, proc0_field);
/* Normalize using the maximum RMS of the HRIRs. The RMS measure for the
* non-filtered signal is of an impulse with equal length (to the filter):
*
* rms_impulse = sqrt(sum([ 1^2, 0^2, 0^2, ... ]) / n)
* = sqrt(1 / n)
*
* This helps keep a more consistent volume between the non-filtered signal
* and various data sets.
*/
double factor{std::sqrt(1.0 / irSize) / maxlev.rms};
/* Also ensure the samples themselves won't clip. */
factor = std::min(factor, 0.99/maxlev.amp);
/* Now scale all IRs by the given factor. */
auto proc1_field = [channels,irSize,factor](HrirFdT &field) -> void
{
auto proc_elev = [channels,irSize,factor](HrirEvT &elev) -> void
{
auto proc_azi = [channels,irSize,factor](HrirAzT &azi) -> void
{
auto proc_channel = [irSize,factor](double *ir) -> void
{
std::transform(ir, ir+irSize, ir,
std::bind(std::multiplies<double>{}, _1, factor));
};
std::for_each(azi.mIrs, azi.mIrs+channels, proc_channel);
};
std::for_each(elev.mAzs, elev.mAzs+elev.mAzCount, proc_azi);
};
std::for_each(field.mEvs, field.mEvs+field.mEvCount, proc_elev);
};
std::for_each(hData->mFds.begin(), hData->mFds.begin()+hData->mFdCount, proc1_field);
}
// Calculate the left-ear time delay using a spherical head model.
static double CalcLTD(const double ev, const double az, const double rad, const double dist)
{
double azp, dlp, l, al;
azp = std::asin(std::cos(ev) * std::sin(az));
dlp = std::sqrt((dist*dist) + (rad*rad) + (2.0*dist*rad*sin(azp)));
l = std::sqrt((dist*dist) - (rad*rad));
al = (0.5 * M_PI) + azp;
if(dlp > l)
dlp = l + (rad * (al - std::acos(rad / dist)));
return dlp / 343.3;
}
// Calculate the effective head-related time delays for each minimum-phase
// HRIR. This is done per-field since distance delay is ignored.
static void CalculateHrtds(const HeadModelT model, const double radius, HrirDataT *hData)
{
uint channels = (hData->mChannelType == CT_STEREO) ? 2 : 1;
double customRatio{radius / hData->mRadius};
uint ti, fi, ei, ai;
if(model == HM_SPHERE)
{
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
HrirEvT *evd = &hData->mFds[fi].mEvs[ei];
for(ai = 0;ai < evd->mAzCount;ai++)
{
HrirAzT *azd = &evd->mAzs[ai];
for(ti = 0;ti < channels;ti++)
azd->mDelays[ti] = CalcLTD(evd->mElevation, azd->mAzimuth, radius, hData->mFds[fi].mDistance);
}
}
}
}
else if(customRatio != 1.0)
{
for(fi = 0;fi < hData->mFdCount;fi++)
{
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
HrirEvT *evd = &hData->mFds[fi].mEvs[ei];
for(ai = 0;ai < evd->mAzCount;ai++)
{
HrirAzT *azd = &evd->mAzs[ai];
for(ti = 0;ti < channels;ti++)
azd->mDelays[ti] *= customRatio;
}
}
}
}
for(fi = 0;fi < hData->mFdCount;fi++)
{
double minHrtd{std::numeric_limits<double>::infinity()};
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
{
HrirAzT *azd = &hData->mFds[fi].mEvs[ei].mAzs[ai];
for(ti = 0;ti < channels;ti++)
minHrtd = std::min(azd->mDelays[ti], minHrtd);
}
}
for(ei = 0;ei < hData->mFds[fi].mEvCount;ei++)
{
for(ti = 0;ti < channels;ti++)
{
for(ai = 0;ai < hData->mFds[fi].mEvs[ei].mAzCount;ai++)
hData->mFds[fi].mEvs[ei].mAzs[ai].mDelays[ti] -= minHrtd;
}
}
}
}
// Allocate and configure dynamic HRIR structures.
int PrepareHrirData(const uint fdCount, const double (&distances)[MAX_FD_COUNT],
const uint (&evCounts)[MAX_FD_COUNT], const uint azCounts[MAX_FD_COUNT * MAX_EV_COUNT],
HrirDataT *hData)
{
uint evTotal = 0, azTotal = 0, fi, ei, ai;
for(fi = 0;fi < fdCount;fi++)
{
evTotal += evCounts[fi];
for(ei = 0;ei < evCounts[fi];ei++)
azTotal += azCounts[(fi * MAX_EV_COUNT) + ei];
}
if(!fdCount || !evTotal || !azTotal)
return 0;
hData->mEvsBase.resize(evTotal);
hData->mAzsBase.resize(azTotal);
hData->mFds.resize(fdCount);
hData->mIrCount = azTotal;
hData->mFdCount = fdCount;
evTotal = 0;
azTotal = 0;
for(fi = 0;fi < fdCount;fi++)
{
hData->mFds[fi].mDistance = distances[fi];
hData->mFds[fi].mEvCount = evCounts[fi];
hData->mFds[fi].mEvStart = 0;
hData->mFds[fi].mEvs = &hData->mEvsBase[evTotal];
evTotal += evCounts[fi];
for(ei = 0;ei < evCounts[fi];ei++)
{
uint azCount = azCounts[(fi * MAX_EV_COUNT) + ei];
hData->mFds[fi].mIrCount += azCount;
hData->mFds[fi].mEvs[ei].mElevation = -M_PI / 2.0 + M_PI * ei / (evCounts[fi] - 1);
hData->mFds[fi].mEvs[ei].mIrCount += azCount;
hData->mFds[fi].mEvs[ei].mAzCount = azCount;
hData->mFds[fi].mEvs[ei].mAzs = &hData->mAzsBase[azTotal];
for(ai = 0;ai < azCount;ai++)
{
hData->mFds[fi].mEvs[ei].mAzs[ai].mAzimuth = 2.0 * M_PI * ai / azCount;
hData->mFds[fi].mEvs[ei].mAzs[ai].mIndex = azTotal + ai;
hData->mFds[fi].mEvs[ei].mAzs[ai].mDelays[0] = 0.0;
hData->mFds[fi].mEvs[ei].mAzs[ai].mDelays[1] = 0.0;
hData->mFds[fi].mEvs[ei].mAzs[ai].mIrs[0] = nullptr;
hData->mFds[fi].mEvs[ei].mAzs[ai].mIrs[1] = nullptr;
}
azTotal += azCount;
}
}
return 1;
}
/* Parse the data set definition and process the source data, storing the
* resulting data set as desired. If the input name is NULL it will read
* from standard input.
*/
static int ProcessDefinition(const char *inName, const uint outRate, const ChannelModeT chanMode, const uint fftSize, const int equalize, const int surface, const double limit, const uint truncSize, const HeadModelT model, const double radius, const char *outName)
{
char rateStr[8+1], expName[MAX_PATH_LEN];
char startbytes[4]{};
size_t startbytecount{0u};
HrirDataT hData;
FILE *fp;
int ret;
if(!inName)
{
inName = "stdin";
fp = stdin;
}
else
{
fp = fopen(inName, "r");
if(fp == nullptr)
{
fprintf(stderr, "Error: Could not open input file '%s'\n", inName);
return 0;
}
startbytecount = fread(startbytes, 1, sizeof(startbytes), fp);
if(startbytecount != sizeof(startbytes))
{
fclose(fp);
fprintf(stderr, "Error: Could not read input file '%s'\n", inName);
return 0;
}
if(startbytes[0] == '\x89' && startbytes[1] == 'H' && startbytes[2] == 'D' &&
startbytes[3] == 'F')
{
fclose(fp);
fp = nullptr;
fprintf(stdout, "Reading HRTF data from %s...\n", inName);
if(!LoadSofaFile(inName, fftSize, truncSize, chanMode, &hData))
return 0;
}
}
if(fp != nullptr)
{
fprintf(stdout, "Reading HRIR definition from %s...\n", inName);
const bool success{LoadDefInput(fp, startbytes, startbytecount, inName, fftSize, truncSize,
chanMode, &hData)};
if(fp != stdin)
fclose(fp);
if(!success)
return 0;
}
if(equalize)
{
uint c = (hData.mChannelType == CT_STEREO) ? 2 : 1;
uint m = 1 + hData.mFftSize / 2;
std::vector<double> dfa(c * m);
if(hData.mFdCount > 1)
{
fprintf(stdout, "Balancing field magnitudes...\n");
BalanceFieldMagnitudes(&hData, c, m);
}
fprintf(stdout, "Calculating diffuse-field average...\n");
CalculateDiffuseFieldAverage(&hData, c, m, surface, limit, dfa.data());
fprintf(stdout, "Performing diffuse-field equalization...\n");
DiffuseFieldEqualize(c, m, dfa.data(), &hData);
}
fprintf(stdout, "Performing minimum phase reconstruction...\n");
ReconstructHrirs(&hData);
if(outRate != 0 && outRate != hData.mIrRate)
{
fprintf(stdout, "Resampling HRIRs...\n");
ResampleHrirs(outRate, &hData);
}
fprintf(stdout, "Truncating minimum-phase HRIRs...\n");
hData.mIrPoints = truncSize;
fprintf(stdout, "Synthesizing missing elevations...\n");
if(model == HM_DATASET)
SynthesizeOnsets(&hData);
SynthesizeHrirs(&hData);
fprintf(stdout, "Normalizing final HRIRs...\n");
NormalizeHrirs(&hData);
fprintf(stdout, "Calculating impulse delays...\n");
CalculateHrtds(model, (radius > DEFAULT_CUSTOM_RADIUS) ? radius : hData.mRadius, &hData);
snprintf(rateStr, 8, "%u", hData.mIrRate);
StrSubst(outName, "%r", rateStr, MAX_PATH_LEN, expName);
fprintf(stdout, "Creating MHR data set %s...\n", expName);
ret = StoreMhr(&hData, expName);
return ret;
}
static void PrintHelp(const char *argv0, FILE *ofile)
{
fprintf(ofile, "Usage: %s [<option>...]\n\n", argv0);
fprintf(ofile, "Options:\n");
fprintf(ofile, " -r <rate> Change the data set sample rate to the specified value and\n");
fprintf(ofile, " resample the HRIRs accordingly.\n");
fprintf(ofile, " -m Change the data set to mono, mirroring the left ear for the\n");
fprintf(ofile, " right ear.\n");
fprintf(ofile, " -f <points> Override the FFT window size (default: %u).\n", DEFAULT_FFTSIZE);
fprintf(ofile, " -e {on|off} Toggle diffuse-field equalization (default: %s).\n", (DEFAULT_EQUALIZE ? "on" : "off"));
fprintf(ofile, " -s {on|off} Toggle surface-weighted diffuse-field average (default: %s).\n", (DEFAULT_SURFACE ? "on" : "off"));
fprintf(ofile, " -l {<dB>|none} Specify a limit to the magnitude range of the diffuse-field\n");
fprintf(ofile, " average (default: %.2f).\n", DEFAULT_LIMIT);
fprintf(ofile, " -w <points> Specify the size of the truncation window that's applied\n");
fprintf(ofile, " after minimum-phase reconstruction (default: %u).\n", DEFAULT_TRUNCSIZE);
fprintf(ofile, " -d {dataset| Specify the model used for calculating the head-delay timing\n");
fprintf(ofile, " sphere} values (default: %s).\n", ((DEFAULT_HEAD_MODEL == HM_DATASET) ? "dataset" : "sphere"));
fprintf(ofile, " -c <radius> Use a customized head radius measured to-ear in meters.\n");
fprintf(ofile, " -i <filename> Specify an HRIR definition file to use (defaults to stdin).\n");
fprintf(ofile, " -o <filename> Specify an output file. Use of '%%r' will be substituted with\n");
fprintf(ofile, " the data set sample rate.\n");
}
// Standard command line dispatch.
int main(int argc, char *argv[])
{
const char *inName = nullptr, *outName = nullptr;
uint outRate, fftSize;
int equalize, surface;
char *end = nullptr;
ChannelModeT chanMode;
HeadModelT model;
uint truncSize;
double radius;
double limit;
int opt;
GET_UNICODE_ARGS(&argc, &argv);
if(argc < 2)
{
fprintf(stdout, "HRTF Processing and Composition Utility\n\n");
PrintHelp(argv[0], stdout);
exit(EXIT_SUCCESS);
}
outName = "./oalsoft_hrtf_%r.mhr";
outRate = 0;
chanMode = CM_AllowStereo;
fftSize = DEFAULT_FFTSIZE;
equalize = DEFAULT_EQUALIZE;
surface = DEFAULT_SURFACE;
limit = DEFAULT_LIMIT;
truncSize = DEFAULT_TRUNCSIZE;
model = DEFAULT_HEAD_MODEL;
radius = DEFAULT_CUSTOM_RADIUS;
while((opt=getopt(argc, argv, "r:mf:e:s:l:w:d:c:e:i:o:h")) != -1)
{
switch(opt)
{
case 'r':
outRate = static_cast<uint>(strtoul(optarg, &end, 10));
if(end[0] != '\0' || outRate < MIN_RATE || outRate > MAX_RATE)
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected between %u to %u.\n", optarg, opt, MIN_RATE, MAX_RATE);
exit(EXIT_FAILURE);
}
break;
case 'm':
chanMode = CM_ForceMono;
break;
case 'f':
fftSize = static_cast<uint>(strtoul(optarg, &end, 10));
if(end[0] != '\0' || (fftSize&(fftSize-1)) || fftSize < MIN_FFTSIZE || fftSize > MAX_FFTSIZE)
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected a power-of-two between %u to %u.\n", optarg, opt, MIN_FFTSIZE, MAX_FFTSIZE);
exit(EXIT_FAILURE);
}
break;
case 'e':
if(strcmp(optarg, "on") == 0)
equalize = 1;
else if(strcmp(optarg, "off") == 0)
equalize = 0;
else
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected on or off.\n", optarg, opt);
exit(EXIT_FAILURE);
}
break;
case 's':
if(strcmp(optarg, "on") == 0)
surface = 1;
else if(strcmp(optarg, "off") == 0)
surface = 0;
else
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected on or off.\n", optarg, opt);
exit(EXIT_FAILURE);
}
break;
case 'l':
if(strcmp(optarg, "none") == 0)
limit = 0.0;
else
{
limit = strtod(optarg, &end);
if(end[0] != '\0' || limit < MIN_LIMIT || limit > MAX_LIMIT)
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected between %.0f to %.0f.\n", optarg, opt, MIN_LIMIT, MAX_LIMIT);
exit(EXIT_FAILURE);
}
}
break;
case 'w':
truncSize = static_cast<uint>(strtoul(optarg, &end, 10));
if(end[0] != '\0' || truncSize < MIN_TRUNCSIZE || truncSize > MAX_TRUNCSIZE || (truncSize%MOD_TRUNCSIZE))
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected multiple of %u between %u to %u.\n", optarg, opt, MOD_TRUNCSIZE, MIN_TRUNCSIZE, MAX_TRUNCSIZE);
exit(EXIT_FAILURE);
}
break;
case 'd':
if(strcmp(optarg, "dataset") == 0)
model = HM_DATASET;
else if(strcmp(optarg, "sphere") == 0)
model = HM_SPHERE;
else
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected dataset or sphere.\n", optarg, opt);
exit(EXIT_FAILURE);
}
break;
case 'c':
radius = strtod(optarg, &end);
if(end[0] != '\0' || radius < MIN_CUSTOM_RADIUS || radius > MAX_CUSTOM_RADIUS)
{
fprintf(stderr, "\nError: Got unexpected value \"%s\" for option -%c, expected between %.2f to %.2f.\n", optarg, opt, MIN_CUSTOM_RADIUS, MAX_CUSTOM_RADIUS);
exit(EXIT_FAILURE);
}
break;
case 'i':
inName = optarg;
break;
case 'o':
outName = optarg;
break;
case 'h':
PrintHelp(argv[0], stdout);
exit(EXIT_SUCCESS);
default: /* '?' */
PrintHelp(argv[0], stderr);
exit(EXIT_FAILURE);
}
}
int ret = ProcessDefinition(inName, outRate, chanMode, fftSize, equalize, surface, limit,
truncSize, model, radius, outName);
if(!ret) return -1;
fprintf(stdout, "Operation completed.\n");
return EXIT_SUCCESS;
}