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Move more processing to the ConvolutionFilter struct

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This commit is contained in:
Chris Robinson
2020-08-31 06:43:42 -07:00
parent 88425becb2
commit af97a92952
1 changed files with 128 additions and 109 deletions
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alc/effects/convolution.cpp
+128 -109
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@@ -97,15 +97,16 @@ using complex_d = std::complex<double>;
constexpr size_t ConvolveUpdateSize{1024};
constexpr size_t ConvolveUpdateSamples{ConvolveUpdateSize / 2};
#define MAX_FILTER_CHANNELS 4
struct ConvolutionFilter final : public EffectBufferBase {
FmtChannels mChannels{};
AmbiLayout mAmbiLayout{};
AmbiScaling mAmbiScaling{};
ALuint mAmbiOrder{};
size_t mFifoPos{0};
al::vector<std::array<double,ConvolveUpdateSamples*2>,16> mOutput;
alignas(16) std::array<complex_d,ConvolveUpdateSize> mFftBuffer{};
size_t mCurrentSegment{0};
size_t mNumConvolveSegs{0};
@@ -123,74 +124,32 @@ struct ConvolutionFilter final : public EffectBufferBase {
ConvolutionFilter(size_t numChannels) : mChans{ChannelDataArray::Create(numChannels)}
{ }
DEF_NEWDEL(ConvolutionFilter)
};
struct ConvolutionState final : public EffectState {
ConvolutionFilter *mFilter{};
size_t mFifoPos{0};
alignas(16) std::array<double,ConvolveUpdateSamples*2> mOutput[MAX_FILTER_CHANNELS]{};
alignas(16) std::array<complex_d,ConvolveUpdateSize> mFftBuffer{};
ConvolutionState() = default;
~ConvolutionState() override = default;
bool init(const ALCdevice *device, const BufferStorage &buffer);
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 (ConvolutionFilter::*mMix)(const al::span<FloatBufferLine>,const size_t)
{&ConvolutionFilter::NormalMix};
void deviceUpdate(const ALCdevice *device) override;
EffectBufferBase *createBuffer(const ALCdevice *device, const BufferStorage &buffer) override;
void update(const ALCcontext *context, const ALeffectslot *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;
void update(al::span<FloatBufferLine> &outTarget, const ALCcontext *context,
const ALeffectslot *slot, const EffectProps *props, const EffectTarget target);
void process(const size_t samplesToDo, const al::span<const FloatBufferLine> samplesIn,
const al::span<FloatBufferLine> samplesOut);
DEF_NEWDEL(ConvolutionState)
DEF_NEWDEL(ConvolutionFilter)
};
void ConvolutionState::NormalMix(const al::span<FloatBufferLine> samplesOut,
const size_t samplesToDo)
bool ConvolutionFilter::init(const ALCdevice *device, const BufferStorage &buffer)
{
auto &chans = *mFilter->mChans;
for(size_t c{0};c < chans.size();++c)
MixSamples({chans[c].mBuffer.data(), samplesToDo}, samplesOut, chans[c].Current,
chans[c].Target, samplesToDo, 0);
}
void ConvolutionState::UpsampleMix(const al::span<FloatBufferLine> samplesOut,
const size_t samplesToDo)
{
auto &chans = *mFilter->mChans;
for(size_t c{0};c < chans.size();++c)
{
const al::span<float> src{chans[c].mBuffer.data(), samplesToDo};
chans[c].mFilter.processHfScale(src, chans[c].mHfScale);
MixSamples(src, samplesOut, chans[c].Current, chans[c].Target, samplesToDo, 0);
}
}
void ConvolutionState::deviceUpdate(const ALCdevice* /*device*/)
{
mFifoPos = 0;
for(auto &buffer : mOutput)
buffer.fill(0.0f);
mFftBuffer.fill(complex_d{});
}
EffectBufferBase *ConvolutionState::createBuffer(const ALCdevice *device,
const BufferStorage &buffer)
{
/* An empty buffer doesn't need a convolution filter. */
if(buffer.mSampleLen < 1) return nullptr;
constexpr size_t m{ConvolveUpdateSize/2 + 1};
/* FIXME: Support anything. */
if(buffer.mChannels != FmtMono && buffer.mChannels != FmtStereo
&& buffer.mChannels != FmtBFormat2D && buffer.mChannels != FmtBFormat3D)
return nullptr;
return false;
if((buffer.mChannels == FmtBFormat2D || buffer.mChannels == FmtBFormat3D)
&& buffer.mAmbiOrder > 1)
return nullptr;
return false;
/* 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
@@ -206,33 +165,31 @@ EffectBufferBase *ConvolutionState::createBuffer(const ALCdevice *device,
auto bytesPerSample = BytesFromFmt(buffer.mType);
auto realChannels = ChannelsFromFmt(buffer.mChannels, buffer.mAmbiOrder);
auto numChannels = ChannelsFromFmt(buffer.mChannels,
minu(buffer.mAmbiOrder, device->mAmbiOrder));
constexpr size_t m{ConvolveUpdateSize/2 + 1};
auto numChannels = mChans->size();
const BandSplitter splitter{400.0f / static_cast<float>(device->Frequency)};
al::intrusive_ptr<ConvolutionFilter> filter{new ConvolutionFilter{numChannels}};
for(auto &e : *filter->mChans)
for(auto &e : *mChans)
e.mFilter = splitter;
mOutput.resize(numChannels, {});
/* Calculate the number of segments needed to hold the impulse response and
* the input history (rounded up), and allocate them.
*/
filter->mNumConvolveSegs = (resampledCount+(ConvolveUpdateSamples-1)) /
ConvolveUpdateSamples;
mNumConvolveSegs = (resampledCount+(ConvolveUpdateSamples-1)) / ConvolveUpdateSamples;
const size_t complex_length{filter->mNumConvolveSegs * m * (numChannels+1)};
filter->mComplexData = std::make_unique<complex_d[]>(complex_length);
std::fill_n(filter->mComplexData.get(), complex_length, complex_d{});
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{});
filter->mChannels = buffer.mChannels;
filter->mAmbiLayout = buffer.mAmbiLayout;
filter->mAmbiScaling = buffer.mAmbiScaling;
filter->mAmbiOrder = buffer.mAmbiOrder;
mChannels = buffer.mChannels;
mAmbiLayout = buffer.mAmbiLayout;
mAmbiScaling = buffer.mAmbiScaling;
mAmbiOrder = buffer.mAmbiOrder;
auto fftbuffer = std::make_unique<std::array<complex_d,ConvolveUpdateSize>>();
auto srcsamples = std::make_unique<double[]>(maxz(buffer.mSampleLen, resampledCount));
complex_d *filteriter = filter->mComplexData.get() + filter->mNumConvolveSegs*m;
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. */
@@ -243,7 +200,7 @@ EffectBufferBase *ConvolutionState::createBuffer(const ALCdevice *device,
srcsamples.get());
size_t done{0};
for(size_t s{0};s < filter->mNumConvolveSegs;++s)
for(size_t s{0};s < mNumConvolveSegs;++s)
{
const size_t todo{minz(resampledCount-done, ConvolveUpdateSamples)};
@@ -255,42 +212,56 @@ EffectBufferBase *ConvolutionState::createBuffer(const ALCdevice *device,
filteriter = std::copy_n(fftbuffer->cbegin(), m, filteriter);
}
}
return filter.release();
return true;
}
void ConvolutionState::update(const ALCcontext *context, const ALeffectslot *slot,
const EffectProps* /*props*/, const EffectTarget target)
void ConvolutionFilter::NormalMix(const al::span<FloatBufferLine> samplesOut,
const size_t samplesToDo)
{
mFilter = static_cast<ConvolutionFilter*>(slot->Params.mEffectBuffer);
if(!mFilter) return;
for(auto &chan : *mChans)
MixSamples({chan.mBuffer.data(), samplesToDo}, samplesOut, chan.Current, chan.Target,
samplesToDo, 0);
}
void ConvolutionFilter::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 ConvolutionFilter::update(al::span<FloatBufferLine> &outTarget, const ALCcontext *context,
const ALeffectslot *slot, const EffectProps* /*props*/, const EffectTarget target)
{
ALCdevice *device{context->mDevice.get()};
mMix = &ConvolutionState::NormalMix;
mMix = &ConvolutionFilter::NormalMix;
/* The iFFT'd response is scaled up by the number of bins, so apply the
* inverse to the output mixing gain.
*/
constexpr size_t m{ConvolveUpdateSize/2 + 1};
const float gain{slot->Params.Gain * (1.0f/m)};
auto &chans = *mFilter->mChans;
if(mFilter->mChannels == FmtBFormat3D || mFilter->mChannels == FmtBFormat2D)
auto &chans = *mChans;
if(mChannels == FmtBFormat3D || mChannels == FmtBFormat2D)
{
if(device->mAmbiOrder > mFilter->mAmbiOrder)
if(device->mAmbiOrder > mAmbiOrder)
{
mMix = &ConvolutionState::UpsampleMix;
const auto scales = BFormatDec::GetHFOrderScales(mFilter->mAmbiOrder,
device->mAmbiOrder);
mMix = &ConvolutionFilter::UpsampleMix;
const auto scales = BFormatDec::GetHFOrderScales(mAmbiOrder, device->mAmbiOrder);
chans[0].mHfScale = scales[0];
for(size_t i{1};i < chans.size();++i)
chans[i].mHfScale = scales[1];
}
mOutTarget = target.Main->Buffer;
outTarget = target.Main->Buffer;
const auto &scales = GetAmbiScales(mFilter->mAmbiScaling);
const uint8_t *index_map{(mFilter->mChannels == FmtBFormat2D) ?
GetAmbi2DLayout(mFilter->mAmbiLayout).data() :
GetAmbiLayout(mFilter->mAmbiLayout).data()};
const auto &scales = GetAmbiScales(mAmbiScaling);
const uint8_t *index_map{(mChannels == FmtBFormat2D) ?
GetAmbi2DLayout(mAmbiLayout).data() :
GetAmbiLayout(mAmbiLayout).data()};
std::array<float,MAX_AMBI_CHANNELS> coeffs{};
for(size_t c{0u};c < chans.size();++c)
@@ -301,7 +272,7 @@ void ConvolutionState::update(const ALCcontext *context, const ALeffectslot *slo
coeffs[acn] = 0.0f;
}
}
else if(mFilter->mChannels == FmtStereo)
else if(mChannels == FmtStereo)
{
/* TODO: Add a "direct channels" setting for this effect? */
const ALuint lidx{!target.RealOut ? INVALID_CHANNEL_INDEX :
@@ -310,7 +281,7 @@ void ConvolutionState::update(const ALCcontext *context, const ALeffectslot *slo
GetChannelIdxByName(*target.RealOut, FrontRight)};
if(lidx != INVALID_CHANNEL_INDEX && ridx != INVALID_CHANNEL_INDEX)
{
mOutTarget = target.RealOut->Buffer;
outTarget = target.RealOut->Buffer;
chans[0].Target[lidx] = gain;
chans[1].Target[ridx] = gain;
}
@@ -319,29 +290,26 @@ void ConvolutionState::update(const ALCcontext *context, const ALeffectslot *slo
const auto lcoeffs = CalcDirectionCoeffs({-1.0f, 0.0f, 0.0f}, 0.0f);
const auto rcoeffs = CalcDirectionCoeffs({ 1.0f, 0.0f, 0.0f}, 0.0f);
mOutTarget = target.Main->Buffer;
outTarget = target.Main->Buffer;
ComputePanGains(target.Main, lcoeffs.data(), gain, chans[0].Target);
ComputePanGains(target.Main, rcoeffs.data(), gain, chans[1].Target);
}
}
else if(mFilter->mChannels == FmtMono)
else if(mChannels == FmtMono)
{
const auto coeffs = CalcDirectionCoeffs({0.0f, 0.0f, -1.0f}, 0.0f);
mOutTarget = target.Main->Buffer;
outTarget = target.Main->Buffer;
ComputePanGains(target.Main, coeffs.data(), gain, chans[0].Target);
}
}
void ConvolutionState::process(const size_t samplesToDo,
void ConvolutionFilter::process(const size_t samplesToDo,
const al::span<const FloatBufferLine> samplesIn, const al::span<FloatBufferLine> samplesOut)
{
/* No filter, no response. */
if(!mFilter) return;
constexpr size_t m{ConvolveUpdateSize/2 + 1};
size_t curseg{mFilter->mCurrentSegment};
auto &chans = *mFilter->mChans;
size_t curseg{mCurrentSegment};
auto &chans = *mChans;
for(size_t base{0u};base < samplesToDo;)
{
@@ -370,22 +338,22 @@ void ConvolutionState::process(const size_t samplesToDo,
*/
complex_fft(mFftBuffer, -1.0);
std::copy_n(mFftBuffer.begin(), m, &mFilter->mComplexData[curseg*m]);
std::copy_n(mFftBuffer.begin(), m, &mComplexData[curseg*m]);
mFftBuffer.fill(complex_d{});
const complex_d *RESTRICT filter{mFilter->mComplexData.get() + mFilter->mNumConvolveSegs*m};
const complex_d *RESTRICT filter{mComplexData.get() + mNumConvolveSegs*m};
for(size_t c{0};c < chans.size();++c)
{
/* Convolve each input segment with its IR filter counterpart
* (aligned in time).
*/
const complex_d *RESTRICT input{&mFilter->mComplexData[curseg*m]};
for(size_t s{curseg};s < mFilter->mNumConvolveSegs;++s)
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 = mFilter->mComplexData.get();
input = mComplexData.get();
for(size_t s{0};s < curseg;++s)
{
for(size_t i{0};i < m;++i,++input,++filter)
@@ -407,15 +375,66 @@ void ConvolutionState::process(const size_t samplesToDo,
}
/* Shift the input history. */
curseg = curseg ? (curseg-1) : (mFilter->mNumConvolveSegs-1);
curseg = curseg ? (curseg-1) : (mNumConvolveSegs-1);
}
mFilter->mCurrentSegment = curseg;
mCurrentSegment = curseg;
/* Finally, mix to the output. */
(this->*mMix)(samplesOut, samplesToDo);
}
struct ConvolutionState final : public EffectState {
ConvolutionFilter *mFilter{};
ConvolutionState() = default;
~ConvolutionState() override = default;
void deviceUpdate(const ALCdevice *device) override;
EffectBufferBase *createBuffer(const ALCdevice *device, const BufferStorage &buffer) override;
void update(const ALCcontext *context, const ALeffectslot *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::deviceUpdate(const ALCdevice* /*device*/)
{
}
EffectBufferBase *ConvolutionState::createBuffer(const ALCdevice *device,
const BufferStorage &buffer)
{
/* An empty buffer doesn't need a convolution filter. */
if(buffer.mSampleLen < 1) return nullptr;
auto numChannels = ChannelsFromFmt(buffer.mChannels,
minu(buffer.mAmbiOrder, device->mAmbiOrder));
al::intrusive_ptr<ConvolutionFilter> filter{new ConvolutionFilter{numChannels}};
if LIKELY(filter->init(device, buffer))
return filter.release();
return nullptr;
}
void ConvolutionState::update(const ALCcontext *context, const ALeffectslot *slot,
const EffectProps *props, const EffectTarget target)
{
mFilter = static_cast<ConvolutionFilter*>(slot->Params.mEffectBuffer);
if(!mFilter) return;
mFilter->update(mOutTarget, context, slot, props, target);
}
void ConvolutionState::process(const size_t samplesToDo,
const al::span<const FloatBufferLine> samplesIn, const al::span<FloatBufferLine> samplesOut)
{
if(mFilter)
mFilter->process(samplesToDo, samplesIn, samplesOut);
}
void ConvolutionEffect_setParami(EffectProps* /*props*/, ALenum param, int /*val*/)
{
switch(param)