#include "config.h"
#include <algorithm>
#include <array>
#include <complex>
#include <cstddef>
#include <functional>
#include <iterator>
#include <memory>
#include <stdint.h>
#include <utility>
#ifdef HAVE_SSE_INTRINSICS
#include <xmmintrin.h>
#elif defined(HAVE_NEON)
#include <arm_neon.h>
#endif
#include "alcomplex.h"
#include "almalloc.h"
#include "alnumbers.h"
#include "alnumeric.h"
#include "alspan.h"
#include "base.h"
#include "core/ambidefs.h"
#include "core/bufferline.h"
#include "core/buffer_storage.h"
#include "core/context.h"
#include "core/devformat.h"
#include "core/device.h"
#include "core/effectslot.h"
#include "core/filters/splitter.h"
#include "core/fmt_traits.h"
#include "core/mixer.h"
#include "intrusive_ptr.h"
#include "pffft.h"
#include "polyphase_resampler.h"
#include "vector.h"
namespace {
/* Convolution is implemented using a segmented overlap-add method. The impulse
* response is split into multiple segments of 128 samples, and each segment
* has an FFT applied with a 256-sample buffer (the latter half left silent) to
* get its frequency-domain response. The resulting response has its positive/
* non-mirrored frequencies saved (129 bins) in each segment. Note that since
* the 0- and half-frequency bins are real for a real signal, their imaginary
* components are always 0 and can be dropped, allowing their real components
* to be combined so only 128 complex values are stored for the 129 bins.
*
* Input samples are similarly broken up into 128-sample segments, with a 256-
* sample FFT applied to each new incoming segment to get its 129 bins. A
* history of FFT'd input segments is maintained, equal to the number of
* impulse response segments.
*
* To apply the convolution, each impulse response segment is convolved with
* its paired input segment (using complex multiplies, far cheaper than FIRs),
* accumulating into a 129-bin FFT buffer. The input history is then shifted to
* align with later impulse response segments for the next input segment.
*
* An inverse FFT is then applied to the accumulated FFT buffer to get a 256-
* sample time-domain response for output, which is split in two halves. The
* first half is the 128-sample output, and the second half is a 128-sample
* (really, 127) delayed extension, which gets added to the output next time.
* Convolving two time-domain responses of length N results in a time-domain
* signal of length N*2 - 1, and this holds true regardless of the convolution
* being applied in the frequency domain, so these "overflow" samples need to
* be accounted for.
*
* To avoid a delay with gathering enough input samples for the FFT, the first
* segment is applied directly in the time-domain as the samples come in. Once
* enough have been retrieved, the FFT is applied on the input and it's paired
* with the remaining (FFT'd) filter segments for processing.
*/
void LoadSamples(float *RESTRICT dst, const std::byte *src, const size_t srcstep, FmtType srctype,
const size_t samples) noexcept
{
#define HANDLE_FMT(T) case T: al::LoadSampleArray<T>(dst, src, srcstep, samples); break
switch(srctype)
{
HANDLE_FMT(FmtUByte);
HANDLE_FMT(FmtShort);
HANDLE_FMT(FmtInt);
HANDLE_FMT(FmtFloat);
HANDLE_FMT(FmtDouble);
HANDLE_FMT(FmtMulaw);
HANDLE_FMT(FmtAlaw);
/* FIXME: Handle ADPCM decoding here. */
case FmtIMA4:
case FmtMSADPCM:
std::fill_n(dst, samples, 0.0f);
break;
}
#undef HANDLE_FMT
}
constexpr auto GetAmbiScales(AmbiScaling scaletype) noexcept
{
switch(scaletype)
{
case AmbiScaling::FuMa: return al::span{AmbiScale::FromFuMa};
case AmbiScaling::SN3D: return al::span{AmbiScale::FromSN3D};
case AmbiScaling::UHJ: return al::span{AmbiScale::FromUHJ};
case AmbiScaling::N3D: break;
}
return al::span{AmbiScale::FromN3D};
}
constexpr auto GetAmbiLayout(AmbiLayout layouttype) noexcept
{
if(layouttype == AmbiLayout::FuMa) return al::span{AmbiIndex::FromFuMa};
return al::span{AmbiIndex::FromACN};
}
constexpr auto GetAmbi2DLayout(AmbiLayout layouttype) noexcept
{
if(layouttype == AmbiLayout::FuMa) return al::span{AmbiIndex::FromFuMa2D};
return al::span{AmbiIndex::FromACN2D};
}
constexpr float sin30{0.5f};
constexpr float cos30{0.866025403785f};
constexpr float sin45{al::numbers::sqrt2_v<float>*0.5f};
constexpr float cos45{al::numbers::sqrt2_v<float>*0.5f};
constexpr float sin110{ 0.939692620786f};
constexpr float cos110{-0.342020143326f};
struct ChanPosMap {
Channel channel;
std::array<float,3> pos;
};
using complex_f = std::complex<float>;
constexpr size_t ConvolveUpdateSize{256};
constexpr size_t ConvolveUpdateSamples{ConvolveUpdateSize / 2};
void apply_fir(al::span<float> dst, const float *RESTRICT src, const float *RESTRICT filter)
{
#ifdef HAVE_SSE_INTRINSICS
for(float &output : dst)
{
__m128 r4{_mm_setzero_ps()};
for(size_t j{0};j < ConvolveUpdateSamples;j+=4)
{
const __m128 coeffs{_mm_load_ps(&filter[j])};
const __m128 s{_mm_loadu_ps(&src[j])};
r4 = _mm_add_ps(r4, _mm_mul_ps(s, coeffs));
}
r4 = _mm_add_ps(r4, _mm_shuffle_ps(r4, r4, _MM_SHUFFLE(0, 1, 2, 3)));
r4 = _mm_add_ps(r4, _mm_movehl_ps(r4, r4));
output = _mm_cvtss_f32(r4);
++src;
}
#elif defined(HAVE_NEON)
for(float &output : dst)
{
float32x4_t r4{vdupq_n_f32(0.0f)};
for(size_t j{0};j < ConvolveUpdateSamples;j+=4)
r4 = vmlaq_f32(r4, vld1q_f32(&src[j]), vld1q_f32(&filter[j]));
r4 = vaddq_f32(r4, vrev64q_f32(r4));
output = vget_lane_f32(vadd_f32(vget_low_f32(r4), vget_high_f32(r4)), 0);
++src;
}
#else
for(float &output : dst)
{
float ret{0.0f};
for(size_t j{0};j < ConvolveUpdateSamples;++j)
ret += src[j] * filter[j];
output = ret;
++src;
}
#endif
}
struct ConvolutionState final : public EffectState {
FmtChannels mChannels{};
AmbiLayout mAmbiLayout{};
AmbiScaling mAmbiScaling{};
uint mAmbiOrder{};
size_t mFifoPos{0};
alignas(16) std::array<float,ConvolveUpdateSamples*2> mInput{};
al::vector<std::array<float,ConvolveUpdateSamples>,16> mFilter;
al::vector<std::array<float,ConvolveUpdateSamples*2>,16> mOutput;
PFFFTSetup mFft{};
alignas(16) std::array<float,ConvolveUpdateSize> mFftBuffer{};
alignas(16) std::array<float,ConvolveUpdateSize> mFftWorkBuffer{};
size_t mCurrentSegment{0};
size_t mNumConvolveSegs{0};
struct ChannelData {
alignas(16) FloatBufferLine mBuffer{};
float mHfScale{}, mLfScale{};
BandSplitter mFilter{};
std::array<float,MaxOutputChannels> Current{};
std::array<float,MaxOutputChannels> Target{};
};
std::vector<ChannelData> mChans;
al::vector<float,16> mComplexData;
ConvolutionState() = default;
~ConvolutionState() override = default;
void NormalMix(const al::span<FloatBufferLine> samplesOut, const size_t samplesToDo);
void UpsampleMix(const al::span<FloatBufferLine> samplesOut, const size_t samplesToDo);
void (ConvolutionState::*mMix)(const al::span<FloatBufferLine>,const size_t)
{&ConvolutionState::NormalMix};
void deviceUpdate(const DeviceBase *device, const BufferStorage *buffer) override;
void update(const ContextBase *context, const EffectSlot *slot, const EffectProps *props,
const EffectTarget target) override;
void process(const size_t samplesToDo, const al::span<const FloatBufferLine> samplesIn,
const al::span<FloatBufferLine> samplesOut) override;
DEF_NEWDEL(ConvolutionState)
};
void ConvolutionState::NormalMix(const al::span<FloatBufferLine> samplesOut,
const size_t samplesToDo)
{
for(auto &chan : mChans)
MixSamples({chan.mBuffer.data(), samplesToDo}, samplesOut, chan.Current.data(),
chan.Target.data(), samplesToDo, 0);
}
void ConvolutionState::UpsampleMix(const al::span<FloatBufferLine> samplesOut,
const size_t samplesToDo)
{
for(auto &chan : mChans)
{
const al::span<float> src{chan.mBuffer.data(), samplesToDo};
chan.mFilter.processScale(src, chan.mHfScale, chan.mLfScale);
MixSamples(src, samplesOut, chan.Current.data(), chan.Target.data(), samplesToDo, 0);
}
}
void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorage *buffer)
{
using UhjDecoderType = UhjDecoder<512>;
static constexpr auto DecoderPadding = UhjDecoderType::sInputPadding;
static constexpr uint MaxConvolveAmbiOrder{1u};
if(!mFft)
mFft = PFFFTSetup{ConvolveUpdateSize, PFFFT_REAL};
mFifoPos = 0;
mInput.fill(0.0f);
decltype(mFilter){}.swap(mFilter);
decltype(mOutput){}.swap(mOutput);
mFftBuffer.fill(0.0f);
mFftWorkBuffer.fill(0.0f);
mCurrentSegment = 0;
mNumConvolveSegs = 0;
decltype(mChans){}.swap(mChans);
decltype(mComplexData){}.swap(mComplexData);
/* An empty buffer doesn't need a convolution filter. */
if(!buffer || buffer->mSampleLen < 1) return;
mChannels = buffer->mChannels;
mAmbiLayout = IsUHJ(mChannels) ? AmbiLayout::FuMa : buffer->mAmbiLayout;
mAmbiScaling = IsUHJ(mChannels) ? AmbiScaling::UHJ : buffer->mAmbiScaling;
mAmbiOrder = minu(buffer->mAmbiOrder, MaxConvolveAmbiOrder);
const auto bytesPerSample = BytesFromFmt(buffer->mType);
const auto realChannels = buffer->channelsFromFmt();
const auto numChannels = (mChannels == FmtUHJ2) ? 3u : ChannelsFromFmt(mChannels, mAmbiOrder);
mChans.resize(numChannels);
/* The impulse response needs to have the same sample rate as the input and
* output. The bsinc24 resampler is decent, but there is high-frequency
* attenuation that some people may be able to pick up on. Since this is
* called very infrequently, go ahead and use the polyphase resampler.
*/
PPhaseResampler resampler;
if(device->Frequency != buffer->mSampleRate)
resampler.init(buffer->mSampleRate, device->Frequency);
const auto resampledCount = static_cast<uint>(
(uint64_t{buffer->mSampleLen}*device->Frequency+(buffer->mSampleRate-1)) /
buffer->mSampleRate);
const BandSplitter splitter{device->mXOverFreq / static_cast<float>(device->Frequency)};
for(auto &e : mChans)
e.mFilter = splitter;
mFilter.resize(numChannels, {});
mOutput.resize(numChannels, {});
/* Calculate the number of segments needed to hold the impulse response and
* the input history (rounded up), and allocate them. Exclude one segment
* which gets applied as a time-domain FIR filter. Make sure at least one
* segment is allocated to simplify handling.
*/
mNumConvolveSegs = (resampledCount+(ConvolveUpdateSamples-1)) / ConvolveUpdateSamples;
mNumConvolveSegs = maxz(mNumConvolveSegs, 2) - 1;
const size_t complex_length{mNumConvolveSegs * ConvolveUpdateSize * (numChannels+1)};
mComplexData.resize(complex_length, 0.0f);
/* Load the samples from the buffer. */
const size_t srclinelength{RoundUp(buffer->mSampleLen+DecoderPadding, 16)};
auto srcsamples = std::make_unique<float[]>(srclinelength * numChannels);
std::fill_n(srcsamples.get(), srclinelength * numChannels, 0.0f);
for(size_t c{0};c < numChannels && c < realChannels;++c)
LoadSamples(srcsamples.get() + srclinelength*c, buffer->mData.data() + bytesPerSample*c,
realChannels, buffer->mType, buffer->mSampleLen);
if(IsUHJ(mChannels))
{
auto decoder = std::make_unique<UhjDecoderType>();
std::array<float*,4> samples{};
for(size_t c{0};c < numChannels;++c)
samples[c] = srcsamples.get() + srclinelength*c;
decoder->decode({samples.data(), numChannels}, buffer->mSampleLen, buffer->mSampleLen);
}
auto ressamples = std::make_unique<double[]>(buffer->mSampleLen +
(resampler ? resampledCount : 0));
auto ffttmp = al::vector<float,16>(ConvolveUpdateSize);
auto fftbuffer = std::vector<std::complex<double>>(ConvolveUpdateSize);
float *filteriter = mComplexData.data() + mNumConvolveSegs*ConvolveUpdateSize;
for(size_t c{0};c < numChannels;++c)
{
/* Resample to match the device. */
if(resampler)
{
std::copy_n(srcsamples.get() + srclinelength*c, buffer->mSampleLen,
ressamples.get() + resampledCount);
resampler.process(buffer->mSampleLen, ressamples.get()+resampledCount,
resampledCount, ressamples.get());
}
else
std::copy_n(srcsamples.get() + srclinelength*c, buffer->mSampleLen, ressamples.get());
/* Store the first segment's samples in reverse in the time-domain, to
* apply as a FIR filter.
*/
const size_t first_size{minz(resampledCount, ConvolveUpdateSamples)};
std::transform(ressamples.get(), ressamples.get()+first_size, mFilter[c].rbegin(),
[](const double d) noexcept -> float { return static_cast<float>(d); });
size_t done{first_size};
for(size_t s{0};s < mNumConvolveSegs;++s)
{
const size_t todo{minz(resampledCount-done, ConvolveUpdateSamples)};
/* Apply a double-precision forward FFT for more precise frequency
* measurements.
*/
auto iter = std::copy_n(&ressamples[done], todo, fftbuffer.begin());
done += todo;
std::fill(iter, fftbuffer.end(), std::complex<double>{});
forward_fft(al::span{fftbuffer});
/* Convert to, and pack in, a float buffer for PFFFT. Note that the
* first bin stores the real component of the half-frequency bin in
* the imaginary component. Also scale the FFT by its length so the
* iFFT'd output will be normalized.
*/
static constexpr float fftscale{1.0f / float{ConvolveUpdateSize}};
for(size_t i{0};i < ConvolveUpdateSamples;++i)
{
ffttmp[i*2 ] = static_cast<float>(fftbuffer[i].real()) * fftscale;
ffttmp[i*2 + 1] = static_cast<float>((i == 0) ?
fftbuffer[ConvolveUpdateSamples].real() : fftbuffer[i].imag()) * fftscale;
}
/* Reorder backward to make it suitable for pffft_zconvolve and the
* subsequent pffft_transform(..., PFFFT_BACKWARD).
*/
mFft.zreorder(ffttmp.data(), al::to_address(filteriter), PFFFT_BACKWARD);
filteriter += ConvolveUpdateSize;
}
}
}
void ConvolutionState::update(const ContextBase *context, const EffectSlot *slot,
const EffectProps *props, const EffectTarget target)
{
/* TODO: LFE is not mixed to output. This will require each buffer channel
* to have its own output target since the main mixing buffer won't have an
* LFE channel (due to being B-Format).
*/
static constexpr ChanPosMap MonoMap[1]{
{ FrontCenter, std::array{0.0f, 0.0f, -1.0f} }
}, StereoMap[2]{
{ FrontLeft, std::array{-sin30, 0.0f, -cos30} },
{ FrontRight, std::array{ sin30, 0.0f, -cos30} },
}, RearMap[2]{
{ BackLeft, std::array{-sin30, 0.0f, cos30} },
{ BackRight, std::array{ sin30, 0.0f, cos30} },
}, QuadMap[4]{
{ FrontLeft, std::array{-sin45, 0.0f, -cos45} },
{ FrontRight, std::array{ sin45, 0.0f, -cos45} },
{ BackLeft, std::array{-sin45, 0.0f, cos45} },
{ BackRight, std::array{ sin45, 0.0f, cos45} },
}, X51Map[6]{
{ FrontLeft, std::array{-sin30, 0.0f, -cos30} },
{ FrontRight, std::array{ sin30, 0.0f, -cos30} },
{ FrontCenter, std::array{ 0.0f, 0.0f, -1.0f} },
{ LFE, {} },
{ SideLeft, std::array{-sin110, 0.0f, -cos110} },
{ SideRight, std::array{ sin110, 0.0f, -cos110} },
}, X61Map[7]{
{ FrontLeft, std::array{-sin30, 0.0f, -cos30} },
{ FrontRight, std::array{ sin30, 0.0f, -cos30} },
{ FrontCenter, std::array{ 0.0f, 0.0f, -1.0f} },
{ LFE, {} },
{ BackCenter, std::array{ 0.0f, 0.0f, 1.0f} },
{ SideLeft, std::array{-1.0f, 0.0f, 0.0f} },
{ SideRight, std::array{ 1.0f, 0.0f, 0.0f} },
}, X71Map[8]{
{ FrontLeft, std::array{-sin30, 0.0f, -cos30} },
{ FrontRight, std::array{ sin30, 0.0f, -cos30} },
{ FrontCenter, std::array{ 0.0f, 0.0f, -1.0f} },
{ LFE, {} },
{ BackLeft, std::array{-sin30, 0.0f, cos30} },
{ BackRight, std::array{ sin30, 0.0f, cos30} },
{ SideLeft, std::array{ -1.0f, 0.0f, 0.0f} },
{ SideRight, std::array{ 1.0f, 0.0f, 0.0f} },
};
if(mNumConvolveSegs < 1) UNLIKELY
return;
mMix = &ConvolutionState::NormalMix;
for(auto &chan : mChans)
std::fill(std::begin(chan.Target), std::end(chan.Target), 0.0f);
const float gain{slot->Gain};
if(IsAmbisonic(mChannels))
{
DeviceBase *device{context->mDevice};
if(mChannels == FmtUHJ2 && !device->mUhjEncoder)
{
mMix = &ConvolutionState::UpsampleMix;
mChans[0].mHfScale = 1.0f;
mChans[0].mLfScale = DecoderBase::sWLFScale;
mChans[1].mHfScale = 1.0f;
mChans[1].mLfScale = DecoderBase::sXYLFScale;
mChans[2].mHfScale = 1.0f;
mChans[2].mLfScale = DecoderBase::sXYLFScale;
}
else if(device->mAmbiOrder > mAmbiOrder)
{
mMix = &ConvolutionState::UpsampleMix;
const auto scales = AmbiScale::GetHFOrderScales(mAmbiOrder, device->mAmbiOrder,
device->m2DMixing);
mChans[0].mHfScale = scales[0];
mChans[0].mLfScale = 1.0f;
for(size_t i{1};i < mChans.size();++i)
{
mChans[i].mHfScale = scales[1];
mChans[i].mLfScale = 1.0f;
}
}
mOutTarget = target.Main->Buffer;
alu::Vector N{props->Convolution.OrientAt[0], props->Convolution.OrientAt[1],
props->Convolution.OrientAt[2], 0.0f};
N.normalize();
alu::Vector V{props->Convolution.OrientUp[0], props->Convolution.OrientUp[1],
props->Convolution.OrientUp[2], 0.0f};
V.normalize();
/* Build and normalize right-vector */
alu::Vector U{N.cross_product(V)};
U.normalize();
const float mixmatrix[4][4]{
{1.0f, 0.0f, 0.0f, 0.0f},
{0.0f, U[0], -U[1], U[2]},
{0.0f, -V[0], V[1], -V[2]},
{0.0f, -N[0], N[1], -N[2]},
};
const auto scales = GetAmbiScales(mAmbiScaling);
const uint8_t *index_map{Is2DAmbisonic(mChannels) ?
GetAmbi2DLayout(mAmbiLayout).data() :
GetAmbiLayout(mAmbiLayout).data()};
std::array<float,MaxAmbiChannels> coeffs{};
for(size_t c{0u};c < mChans.size();++c)
{
const size_t acn{index_map[c]};
const float scale{scales[acn]};
for(size_t x{0};x < 4;++x)
coeffs[x] = mixmatrix[acn][x] * scale;
ComputePanGains(target.Main, coeffs, gain, mChans[c].Target);
}
}
else
{
DeviceBase *device{context->mDevice};
al::span<const ChanPosMap> chanmap{};
switch(mChannels)
{
case FmtMono: chanmap = MonoMap; break;
case FmtSuperStereo:
case FmtStereo: chanmap = StereoMap; break;
case FmtRear: chanmap = RearMap; break;
case FmtQuad: chanmap = QuadMap; break;
case FmtX51: chanmap = X51Map; break;
case FmtX61: chanmap = X61Map; break;
case FmtX71: chanmap = X71Map; break;
case FmtBFormat2D:
case FmtBFormat3D:
case FmtUHJ2:
case FmtUHJ3:
case FmtUHJ4:
break;
}
mOutTarget = target.Main->Buffer;
if(device->mRenderMode == RenderMode::Pairwise)
{
/* Scales the azimuth of the given vector by 3 if it's in front.
* Effectively scales +/-30 degrees to +/-90 degrees, leaving > +90
* and < -90 alone.
*/
auto ScaleAzimuthFront = [](std::array<float,3> pos) -> std::array<float,3>
{
if(pos[2] < 0.0f)
{
/* Normalize the length of the x,z components for a 2D
* vector of the azimuth angle. Negate Z since {0,0,-1} is
* angle 0.
*/
const float len2d{std::sqrt(pos[0]*pos[0] + pos[2]*pos[2])};
float x{pos[0] / len2d};
float z{-pos[2] / len2d};
/* Z > cos(pi/6) = -30 < azimuth < 30 degrees. */
if(z > cos30)
{
/* Triple the angle represented by x,z. */
x = x*3.0f - x*x*x*4.0f;
z = z*z*z*4.0f - z*3.0f;
/* Scale the vector back to fit in 3D. */
pos[0] = x * len2d;
pos[2] = -z * len2d;
}
else
{
/* If azimuth >= 30 degrees, clamp to 90 degrees. */
pos[0] = std::copysign(len2d, pos[0]);
pos[2] = 0.0f;
}
}
return pos;
};
for(size_t i{0};i < chanmap.size();++i)
{
if(chanmap[i].channel == LFE) continue;
const auto coeffs = CalcDirectionCoeffs(ScaleAzimuthFront(chanmap[i].pos), 0.0f);
ComputePanGains(target.Main, coeffs, gain, mChans[i].Target);
}
}
else for(size_t i{0};i < chanmap.size();++i)
{
if(chanmap[i].channel == LFE) continue;
const auto coeffs = CalcDirectionCoeffs(chanmap[i].pos, 0.0f);
ComputePanGains(target.Main, coeffs, gain, mChans[i].Target);
}
}
}
void ConvolutionState::process(const size_t samplesToDo,
const al::span<const FloatBufferLine> samplesIn, const al::span<FloatBufferLine> samplesOut)
{
if(mNumConvolveSegs < 1) UNLIKELY
return;
size_t curseg{mCurrentSegment};
for(size_t base{0u};base < samplesToDo;)
{
const size_t todo{minz(ConvolveUpdateSamples-mFifoPos, samplesToDo-base)};
std::copy_n(samplesIn[0].begin() + base, todo,
mInput.begin()+ConvolveUpdateSamples+mFifoPos);
/* Apply the FIR for the newly retrieved input samples, and combine it
* with the inverse FFT'd output samples.
*/
for(size_t c{0};c < mChans.size();++c)
{
auto buf_iter = mChans[c].mBuffer.begin() + base;
apply_fir({buf_iter, todo}, mInput.data()+1 + mFifoPos, mFilter[c].data());
auto fifo_iter = mOutput[c].begin() + mFifoPos;
std::transform(fifo_iter, fifo_iter+todo, buf_iter, buf_iter, std::plus<>{});
}
mFifoPos += todo;
base += todo;
/* Check whether the input buffer is filled with new samples. */
if(mFifoPos < ConvolveUpdateSamples) break;
mFifoPos = 0;
/* Move the newest input to the front for the next iteration's history. */
std::copy(mInput.cbegin()+ConvolveUpdateSamples, mInput.cend(), mInput.begin());
std::fill(mInput.begin()+ConvolveUpdateSamples, mInput.end(), 0.0f);
/* Calculate the frequency-domain response and add the relevant
* frequency bins to the FFT history.
*/
mFft.transform(mInput.data(), mComplexData.data() + curseg*ConvolveUpdateSize,
mFftWorkBuffer.data(), PFFFT_FORWARD);
const float *filter{mComplexData.data() + mNumConvolveSegs*ConvolveUpdateSize};
for(size_t c{0};c < mChans.size();++c)
{
/* Convolve each input segment with its IR filter counterpart
* (aligned in time).
*/
mFftBuffer.fill(0.0f);
const float *input{&mComplexData[curseg*ConvolveUpdateSize]};
for(size_t s{curseg};s < mNumConvolveSegs;++s)
{
mFft.zconvolve_accumulate(input, filter, mFftBuffer.data());
input += ConvolveUpdateSize;
filter += ConvolveUpdateSize;
}
input = mComplexData.data();
for(size_t s{0};s < curseg;++s)
{
mFft.zconvolve_accumulate(input, filter, mFftBuffer.data());
input += ConvolveUpdateSize;
filter += ConvolveUpdateSize;
}
/* Apply iFFT to get the 256 (really 255) samples for output. The
* 128 output samples are combined with the last output's 127
* second-half samples (and this output's second half is
* subsequently saved for next time).
*/
mFft.transform(mFftBuffer.data(), mFftBuffer.data(), mFftWorkBuffer.data(),
PFFFT_BACKWARD);
/* The filter was attenuated, so the response is already scaled. */
for(size_t i{0};i < ConvolveUpdateSamples;++i)
mOutput[c][i] = mFftBuffer[i] + mOutput[c][ConvolveUpdateSamples+i];
for(size_t i{0};i < ConvolveUpdateSamples;++i)
mOutput[c][ConvolveUpdateSamples+i] = mFftBuffer[ConvolveUpdateSamples+i];
}
/* Shift the input history. */
curseg = curseg ? (curseg-1) : (mNumConvolveSegs-1);
}
mCurrentSegment = curseg;
/* Finally, mix to the output. */
(this->*mMix)(samplesOut, samplesToDo);
}
struct ConvolutionStateFactory final : public EffectStateFactory {
al::intrusive_ptr<EffectState> create() override
{ return al::intrusive_ptr<EffectState>{new ConvolutionState{}}; }
};
} // namespace
EffectStateFactory *ConvolutionStateFactory_getFactory()
{
static ConvolutionStateFactory ConvolutionFactory{};
return &ConvolutionFactory;
}