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axmol/thirdparty/openal/alc/effects/convolution.cpp

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#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 "albyte.h"
#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 "polyphase_resampler.h"
#include "vector.h"
namespace {
/* Convolution reverb is implemented using a segmented overlap-add method. The
* impulse response is broken up 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.
*
* Input samples are similarly broken up into 128-sample segments, with an FFT
* applied to each new incoming segment to get its 129 bins. A history of FFT'd
* input segments is maintained, equal to the length of the impulse response.
*
* To apply the reverberation, each impulse response segment is convolved with
* its paired input segment (using complex multiplies, far cheaper than FIRs),
* accumulating into a 256-bin FFT buffer. The input history is then shifted to
* align with later impulse response segments for next time.
*
* 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 lengths N and M results in a time-
* domain signal of length N+M-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 to apply an FFT with,
* 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(double *RESTRICT dst, const al::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(FmtFloat);
HANDLE_FMT(FmtDouble);
HANDLE_FMT(FmtMulaw);
HANDLE_FMT(FmtAlaw);
}
#undef HANDLE_FMT
}
inline auto& GetAmbiScales(AmbiScaling scaletype) noexcept
{
switch(scaletype)
{
case AmbiScaling::FuMa: return AmbiScale::FromFuMa();
case AmbiScaling::SN3D: return AmbiScale::FromSN3D();
case AmbiScaling::UHJ: return AmbiScale::FromUHJ();
case AmbiScaling::N3D: break;
}
return AmbiScale::FromN3D();
}
inline auto& GetAmbiLayout(AmbiLayout layouttype) noexcept
{
if(layouttype == AmbiLayout::FuMa) return AmbiIndex::FromFuMa();
return AmbiIndex::FromACN();
}
inline auto& GetAmbi2DLayout(AmbiLayout layouttype) noexcept
{
if(layouttype == AmbiLayout::FuMa) return AmbiIndex::FromFuMa2D();
return AmbiIndex::FromACN2D();
}
struct ChanMap {
Channel channel;
float angle;
float elevation;
};
constexpr float Deg2Rad(float x) noexcept
{ return static_cast<float>(al::numbers::pi / 180.0 * x); }
using complex_d = std::complex<double>;
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};
std::array<float,ConvolveUpdateSamples*2> mInput{};
al::vector<std::array<float,ConvolveUpdateSamples>,16> mFilter;
al::vector<std::array<float,ConvolveUpdateSamples*2>,16> mOutput;
alignas(16) std::array<complex_d,ConvolveUpdateSize> mFftBuffer{};
size_t mCurrentSegment{0};
size_t mNumConvolveSegs{0};
struct ChannelData {
alignas(16) FloatBufferLine mBuffer{};
float mHfScale{};
BandSplitter mFilter{};
float Current[MAX_OUTPUT_CHANNELS]{};
float Target[MAX_OUTPUT_CHANNELS]{};
};
using ChannelDataArray = al::FlexArray<ChannelData>;
std::unique_ptr<ChannelDataArray> mChans;
std::unique_ptr<complex_d[]> 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 Buffer &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, chan.Target,
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.processHfScale(src, chan.mHfScale);
MixSamples(src, samplesOut, chan.Current, chan.Target, samplesToDo, 0);
}
}
void ConvolutionState::deviceUpdate(const DeviceBase *device, const Buffer &buffer)
{
constexpr uint MaxConvolveAmbiOrder{1u};
mFifoPos = 0;
mInput.fill(0.0f);
decltype(mFilter){}.swap(mFilter);
decltype(mOutput){}.swap(mOutput);
mFftBuffer.fill(complex_d{});
mCurrentSegment = 0;
mNumConvolveSegs = 0;
mChans = nullptr;
mComplexData = nullptr;
/* An empty buffer doesn't need a convolution filter. */
if(!buffer.storage || buffer.storage->mSampleLen < 1) return;
constexpr size_t m{ConvolveUpdateSize/2 + 1};
auto bytesPerSample = BytesFromFmt(buffer.storage->mType);
auto realChannels = ChannelsFromFmt(buffer.storage->mChannels, buffer.storage->mAmbiOrder);
auto numChannels = ChannelsFromFmt(buffer.storage->mChannels,
minu(buffer.storage->mAmbiOrder, MaxConvolveAmbiOrder));
mChans = ChannelDataArray::Create(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
* attenation 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.storage->mSampleRate)
resampler.init(buffer.storage->mSampleRate, device->Frequency);
const auto resampledCount = static_cast<uint>(
(uint64_t{buffer.storage->mSampleLen}*device->Frequency+(buffer.storage->mSampleRate-1)) /
buffer.storage->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 * m * (numChannels+1)};
mComplexData = std::make_unique<complex_d[]>(complex_length);
std::fill_n(mComplexData.get(), complex_length, complex_d{});
mChannels = buffer.storage->mChannels;
mAmbiLayout = buffer.storage->mAmbiLayout;
mAmbiScaling = buffer.storage->mAmbiScaling;
mAmbiOrder = minu(buffer.storage->mAmbiOrder, MaxConvolveAmbiOrder);
auto srcsamples = std::make_unique<double[]>(maxz(buffer.storage->mSampleLen, resampledCount));
complex_d *filteriter = mComplexData.get() + mNumConvolveSegs*m;
for(size_t c{0};c < numChannels;++c)
{
/* Load the samples from the buffer, and resample to match the device. */
LoadSamples(srcsamples.get(), buffer.samples.data() + bytesPerSample*c, realChannels,
buffer.storage->mType, buffer.storage->mSampleLen);
if(device->Frequency != buffer.storage->mSampleRate)
resampler.process(buffer.storage->mSampleLen, srcsamples.get(), resampledCount,
srcsamples.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(srcsamples.get(), srcsamples.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)};
auto iter = std::copy_n(&srcsamples[done], todo, mFftBuffer.begin());
done += todo;
std::fill(iter, mFftBuffer.end(), complex_d{});
forward_fft(mFftBuffer);
filteriter = std::copy_n(mFftBuffer.cbegin(), m, filteriter);
}
}
}
void ConvolutionState::update(const ContextBase *context, const EffectSlot *slot,
const EffectProps* /*props*/, const EffectTarget target)
{
/* NOTE: Stereo and Rear are slightly different from normal mixing (as
* defined in alu.cpp). These are 45 degrees from center, rather than the
* 30 degrees used there.
*
* 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 ChanMap MonoMap[1]{
{ FrontCenter, 0.0f, 0.0f }
}, StereoMap[2]{
{ FrontLeft, Deg2Rad(-45.0f), Deg2Rad(0.0f) },
{ FrontRight, Deg2Rad( 45.0f), Deg2Rad(0.0f) }
}, RearMap[2]{
{ BackLeft, Deg2Rad(-135.0f), Deg2Rad(0.0f) },
{ BackRight, Deg2Rad( 135.0f), Deg2Rad(0.0f) }
}, QuadMap[4]{
{ FrontLeft, Deg2Rad( -45.0f), Deg2Rad(0.0f) },
{ FrontRight, Deg2Rad( 45.0f), Deg2Rad(0.0f) },
{ BackLeft, Deg2Rad(-135.0f), Deg2Rad(0.0f) },
{ BackRight, Deg2Rad( 135.0f), Deg2Rad(0.0f) }
}, X51Map[6]{
{ FrontLeft, Deg2Rad( -30.0f), Deg2Rad(0.0f) },
{ FrontRight, Deg2Rad( 30.0f), Deg2Rad(0.0f) },
{ FrontCenter, Deg2Rad( 0.0f), Deg2Rad(0.0f) },
{ LFE, 0.0f, 0.0f },
{ SideLeft, Deg2Rad(-110.0f), Deg2Rad(0.0f) },
{ SideRight, Deg2Rad( 110.0f), Deg2Rad(0.0f) }
}, X61Map[7]{
{ FrontLeft, Deg2Rad(-30.0f), Deg2Rad(0.0f) },
{ FrontRight, Deg2Rad( 30.0f), Deg2Rad(0.0f) },
{ FrontCenter, Deg2Rad( 0.0f), Deg2Rad(0.0f) },
{ LFE, 0.0f, 0.0f },
{ BackCenter, Deg2Rad(180.0f), Deg2Rad(0.0f) },
{ SideLeft, Deg2Rad(-90.0f), Deg2Rad(0.0f) },
{ SideRight, Deg2Rad( 90.0f), Deg2Rad(0.0f) }
}, X71Map[8]{
{ FrontLeft, Deg2Rad( -30.0f), Deg2Rad(0.0f) },
{ FrontRight, Deg2Rad( 30.0f), Deg2Rad(0.0f) },
{ FrontCenter, Deg2Rad( 0.0f), Deg2Rad(0.0f) },
{ LFE, 0.0f, 0.0f },
{ BackLeft, Deg2Rad(-150.0f), Deg2Rad(0.0f) },
{ BackRight, Deg2Rad( 150.0f), Deg2Rad(0.0f) },
{ SideLeft, Deg2Rad( -90.0f), Deg2Rad(0.0f) },
{ SideRight, Deg2Rad( 90.0f), Deg2Rad(0.0f) }
};
if(mNumConvolveSegs < 1)
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};
/* TODO: UHJ should be decoded to B-Format and processed that way, since
* there's no telling if it can ever do a direct-out mix (even if the
* device is outputing UHJ, the effect slot can feed another effect that's
* not UHJ).
*
* Not that UHJ should really ever be used for convolution, but it's a
* valid format regardless.
*/
if((mChannels == FmtUHJ2 || mChannels == FmtUHJ3 || mChannels == FmtUHJ4) && target.RealOut
&& target.RealOut->ChannelIndex[FrontLeft] != INVALID_CHANNEL_INDEX
&& target.RealOut->ChannelIndex[FrontRight] != INVALID_CHANNEL_INDEX)
{
mOutTarget = target.RealOut->Buffer;
const uint lidx = target.RealOut->ChannelIndex[FrontLeft];
const uint ridx = target.RealOut->ChannelIndex[FrontRight];
(*mChans)[0].Target[lidx] = gain;
(*mChans)[1].Target[ridx] = gain;
}
else if(IsBFormat(mChannels))
{
DeviceBase *device{context->mDevice};
if(device->mAmbiOrder > mAmbiOrder)
{
mMix = &ConvolutionState::UpsampleMix;
const auto scales = AmbiScale::GetHFOrderScales(mAmbiOrder, device->mAmbiOrder);
(*mChans)[0].mHfScale = scales[0];
for(size_t i{1};i < mChans->size();++i)
(*mChans)[i].mHfScale = scales[1];
}
mOutTarget = target.Main->Buffer;
auto&& scales = GetAmbiScales(mAmbiScaling);
const uint8_t *index_map{(mChannels == FmtBFormat2D) ?
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]};
coeffs[acn] = scales[acn];
ComputePanGains(target.Main, coeffs.data(), gain, (*mChans)[c].Target);
coeffs[acn] = 0.0f;
}
}
else
{
DeviceBase *device{context->mDevice};
al::span<const ChanMap> 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)
{
auto ScaleAzimuthFront = [](float azimuth, float scale) -> float
{
constexpr float half_pi{al::numbers::pi_v<float>*0.5f};
const float abs_azi{std::fabs(azimuth)};
if(!(abs_azi >= half_pi))
return std::copysign(minf(abs_azi*scale, half_pi), azimuth);
return azimuth;
};
for(size_t i{0};i < chanmap.size();++i)
{
if(chanmap[i].channel == LFE) continue;
const auto coeffs = CalcAngleCoeffs(ScaleAzimuthFront(chanmap[i].angle, 2.0f),
chanmap[i].elevation, 0.0f);
ComputePanGains(target.Main, coeffs.data(), gain, (*mChans)[i].Target);
}
}
else for(size_t i{0};i < chanmap.size();++i)
{
if(chanmap[i].channel == LFE) continue;
const auto coeffs = CalcAngleCoeffs(chanmap[i].angle, chanmap[i].elevation, 0.0f);
ComputePanGains(target.Main, coeffs.data(), 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)
return;
constexpr size_t m{ConvolveUpdateSize/2 + 1};
size_t curseg{mCurrentSegment};
auto &chans = *mChans;
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 < chans.size();++c)
{
auto buf_iter = chans[c].mBuffer.begin() + base;
apply_fir({std::addressof(*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());
/* Calculate the frequency domain response and add the relevant
* frequency bins to the FFT history.
*/
auto fftiter = std::copy_n(mInput.cbegin(), ConvolveUpdateSamples, mFftBuffer.begin());
std::fill(fftiter, mFftBuffer.end(), complex_d{});
forward_fft(mFftBuffer);
std::copy_n(mFftBuffer.cbegin(), m, &mComplexData[curseg*m]);
const complex_d *RESTRICT filter{mComplexData.get() + mNumConvolveSegs*m};
for(size_t c{0};c < chans.size();++c)
{
std::fill_n(mFftBuffer.begin(), m, complex_d{});
/* Convolve each input segment with its IR filter counterpart
* (aligned in time).
*/
const complex_d *RESTRICT input{&mComplexData[curseg*m]};
for(size_t s{curseg};s < mNumConvolveSegs;++s)
{
for(size_t i{0};i < m;++i,++input,++filter)
mFftBuffer[i] += *input * *filter;
}
input = mComplexData.get();
for(size_t s{0};s < curseg;++s)
{
for(size_t i{0};i < m;++i,++input,++filter)
mFftBuffer[i] += *input * *filter;
}
/* Reconstruct the mirrored/negative frequencies to do a proper
* inverse FFT.
*/
for(size_t i{m};i < ConvolveUpdateSize;++i)
mFftBuffer[i] = std::conj(mFftBuffer[ConvolveUpdateSize-i]);
/* 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).
*/
inverse_fft(mFftBuffer);
/* The iFFT'd response is scaled up by the number of bins, so apply
* the inverse to normalize the output.
*/
for(size_t i{0};i < ConvolveUpdateSamples;++i)
mOutput[c][i] =
static_cast<float>(mFftBuffer[i].real() * (1.0/double{ConvolveUpdateSize})) +
mOutput[c][ConvolveUpdateSamples+i];
for(size_t i{0};i < ConvolveUpdateSamples;++i)
mOutput[c][ConvolveUpdateSamples+i] =
static_cast<float>(mFftBuffer[ConvolveUpdateSamples+i].real() *
(1.0/double{ConvolveUpdateSize}));
}
/* 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;
}
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