diff options
Diffstat (limited to 'alc/effects/convolution.cpp')
| -rw-r--r-- | alc/effects/convolution.cpp | 160 |
1 files changed, 79 insertions, 81 deletions
diff --git a/alc/effects/convolution.cpp b/alc/effects/convolution.cpp index 517e6b08..3f3e157c 100644 --- a/alc/effects/convolution.cpp +++ b/alc/effects/convolution.cpp @@ -5,11 +5,12 @@ #include <array> #include <complex> #include <cstddef> +#include <cstdint> #include <functional> #include <iterator> #include <memory> -#include <stdint.h> #include <utility> +#include <vector> #ifdef HAVE_SSE_INTRINSICS #include <xmmintrin.h> @@ -190,12 +191,6 @@ void apply_fir(al::span<float> dst, const float *RESTRICT src, const float *REST } -struct PFFFTSetupDeleter { - void operator()(PFFFT_Setup *ptr) { pffft_destroy_setup(ptr); } -}; -using PFFFTSetupPtr = std::unique_ptr<PFFFT_Setup,PFFFTSetupDeleter>; - - struct ConvolutionState final : public EffectState { FmtChannels mChannels{}; AmbiLayout mAmbiLayout{}; @@ -207,7 +202,7 @@ struct ConvolutionState final : public EffectState { al::vector<std::array<float,ConvolveUpdateSamples>,16> mFilter; al::vector<std::array<float,ConvolveUpdateSamples*2>,16> mOutput; - PFFFTSetupPtr mFft{}; + PFFFTSetup mFft{}; alignas(16) std::array<float,ConvolveUpdateSize> mFftBuffer{}; alignas(16) std::array<float,ConvolveUpdateSize> mFftWorkBuffer{}; @@ -218,8 +213,8 @@ struct ConvolutionState final : public EffectState { alignas(16) FloatBufferLine mBuffer{}; float mHfScale{}, mLfScale{}; BandSplitter mFilter{}; - float Current[MAX_OUTPUT_CHANNELS]{}; - float Target[MAX_OUTPUT_CHANNELS]{}; + std::array<float,MaxOutputChannels> Current{}; + std::array<float,MaxOutputChannels> Target{}; }; std::vector<ChannelData> mChans; al::vector<float,16> mComplexData; @@ -238,16 +233,14 @@ struct ConvolutionState final : public EffectState { 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); + MixSamples({chan.mBuffer.data(), samplesToDo}, samplesOut, chan.Current.data(), + chan.Target.data(), samplesToDo, 0); } void ConvolutionState::UpsampleMix(const al::span<FloatBufferLine> samplesOut, @@ -257,7 +250,7 @@ void ConvolutionState::UpsampleMix(const al::span<FloatBufferLine> samplesOut, { const al::span<float> src{chan.mBuffer.data(), samplesToDo}; chan.mFilter.processScale(src, chan.mHfScale, chan.mLfScale); - MixSamples(src, samplesOut, chan.Current, chan.Target, samplesToDo, 0); + MixSamples(src, samplesOut, chan.Current.data(), chan.Target.data(), samplesToDo, 0); } } @@ -270,7 +263,7 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag static constexpr uint MaxConvolveAmbiOrder{1u}; if(!mFft) - mFft = PFFFTSetupPtr{pffft_new_setup(ConvolveUpdateSize, PFFFT_REAL)}; + mFft = PFFFTSetup{ConvolveUpdateSize, PFFFT_REAL}; mFifoPos = 0; mInput.fill(0.0f); @@ -331,10 +324,10 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag /* 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); + auto srcsamples = std::vector<float>(srclinelength * numChannels); + std::fill(srcsamples.begin(), srcsamples.end(), 0.0f); for(size_t c{0};c < numChannels && c < realChannels;++c) - LoadSamples(srcsamples.get() + srclinelength*c, buffer->mData.data() + bytesPerSample*c, + LoadSamples(srcsamples.data() + srclinelength*c, buffer->mData.data() + bytesPerSample*c, realChannels, buffer->mType, buffer->mSampleLen); if(IsUHJ(mChannels)) @@ -342,12 +335,11 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag 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; + samples[c] = srcsamples.data() + srclinelength*c; decoder->decode({samples.data(), numChannels}, buffer->mSampleLen, buffer->mSampleLen); } - auto ressamples = std::make_unique<double[]>(buffer->mSampleLen + - (resampler ? resampledCount : 0)); + auto ressamples = std::vector<double>(buffer->mSampleLen + (resampler ? resampledCount : 0)); auto ffttmp = al::vector<float,16>(ConvolveUpdateSize); auto fftbuffer = std::vector<std::complex<double>>(ConvolveUpdateSize); @@ -357,19 +349,20 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag /* 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()); + std::copy_n(srcsamples.data() + srclinelength*c, buffer->mSampleLen, + ressamples.data() + resampledCount); + resampler.process(buffer->mSampleLen, ressamples.data()+resampledCount, + resampledCount, ressamples.data()); } else - std::copy_n(srcsamples.get() + srclinelength*c, buffer->mSampleLen, ressamples.get()); + std::copy_n(srcsamples.data() + srclinelength*c, buffer->mSampleLen, + ressamples.data()); /* 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(), + std::transform(ressamples.data(), ressamples.data()+first_size, mFilter[c].rbegin(), [](const double d) noexcept -> float { return static_cast<float>(d); }); size_t done{first_size}; @@ -400,7 +393,7 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag /* Reorder backward to make it suitable for pffft_zconvolve and the * subsequent pffft_transform(..., PFFFT_BACKWARD). */ - pffft_zreorder(mFft.get(), ffttmp.data(), al::to_address(filteriter), PFFFT_BACKWARD); + mFft.zreorder(ffttmp.data(), al::to_address(filteriter), PFFFT_BACKWARD); filteriter += ConvolveUpdateSize; } } @@ -408,54 +401,61 @@ void ConvolutionState::deviceUpdate(const DeviceBase *device, const BufferStorag void ConvolutionState::update(const ContextBase *context, const EffectSlot *slot, - const EffectProps *props, const EffectTarget target) + 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} }, + static constexpr std::array MonoMap{ + ChanPosMap{FrontCenter, std::array{0.0f, 0.0f, -1.0f}} + }; + static constexpr std::array StereoMap{ + ChanPosMap{FrontLeft, std::array{-sin30, 0.0f, -cos30}}, + ChanPosMap{FrontRight, std::array{ sin30, 0.0f, -cos30}}, + }; + static constexpr std::array RearMap{ + ChanPosMap{BackLeft, std::array{-sin30, 0.0f, cos30}}, + ChanPosMap{BackRight, std::array{ sin30, 0.0f, cos30}}, + }; + static constexpr std::array QuadMap{ + ChanPosMap{FrontLeft, std::array{-sin45, 0.0f, -cos45}}, + ChanPosMap{FrontRight, std::array{ sin45, 0.0f, -cos45}}, + ChanPosMap{BackLeft, std::array{-sin45, 0.0f, cos45}}, + ChanPosMap{BackRight, std::array{ sin45, 0.0f, cos45}}, + }; + static constexpr std::array X51Map{ + ChanPosMap{FrontLeft, std::array{-sin30, 0.0f, -cos30}}, + ChanPosMap{FrontRight, std::array{ sin30, 0.0f, -cos30}}, + ChanPosMap{FrontCenter, std::array{ 0.0f, 0.0f, -1.0f}}, + ChanPosMap{LFE, {}}, + ChanPosMap{SideLeft, std::array{-sin110, 0.0f, -cos110}}, + ChanPosMap{SideRight, std::array{ sin110, 0.0f, -cos110}}, + }; + static constexpr std::array X61Map{ + ChanPosMap{FrontLeft, std::array{-sin30, 0.0f, -cos30}}, + ChanPosMap{FrontRight, std::array{ sin30, 0.0f, -cos30}}, + ChanPosMap{FrontCenter, std::array{ 0.0f, 0.0f, -1.0f}}, + ChanPosMap{LFE, {}}, + ChanPosMap{BackCenter, std::array{ 0.0f, 0.0f, 1.0f} }, + ChanPosMap{SideLeft, std::array{-1.0f, 0.0f, 0.0f} }, + ChanPosMap{SideRight, std::array{ 1.0f, 0.0f, 0.0f} }, + }; + static constexpr std::array X71Map{ + ChanPosMap{FrontLeft, std::array{-sin30, 0.0f, -cos30}}, + ChanPosMap{FrontRight, std::array{ sin30, 0.0f, -cos30}}, + ChanPosMap{FrontCenter, std::array{ 0.0f, 0.0f, -1.0f}}, + ChanPosMap{LFE, {}}, + ChanPosMap{BackLeft, std::array{-sin30, 0.0f, cos30}}, + ChanPosMap{BackRight, std::array{ sin30, 0.0f, cos30}}, + ChanPosMap{SideLeft, std::array{ -1.0f, 0.0f, 0.0f}}, + ChanPosMap{SideRight, std::array{ 1.0f, 0.0f, 0.0f}}, }; if(mNumConvolveSegs < 1) UNLIKELY return; + auto &props = std::get<ConvolutionProps>(*props_); mMix = &ConvolutionState::NormalMix; for(auto &chan : mChans) @@ -489,21 +489,19 @@ void ConvolutionState::update(const ContextBase *context, const EffectSlot *slot } mOutTarget = target.Main->Buffer; - alu::Vector N{props->Convolution.OrientAt[0], props->Convolution.OrientAt[1], - props->Convolution.OrientAt[2], 0.0f}; + alu::Vector N{props.OrientAt[0], props.OrientAt[1], props.OrientAt[2], 0.0f}; N.normalize(); - alu::Vector V{props->Convolution.OrientUp[0], props->Convolution.OrientUp[1], - props->Convolution.OrientUp[2], 0.0f}; + alu::Vector V{props.OrientUp[0], props.OrientUp[1], props.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 std::array mixmatrix{ + std::array{1.0f, 0.0f, 0.0f, 0.0f}, + std::array{0.0f, U[0], -U[1], U[2]}, + std::array{0.0f, -V[0], V[1], -V[2]}, + std::array{0.0f, -N[0], N[1], -N[2]}, }; const auto scales = GetAmbiScales(mAmbiScaling); @@ -642,7 +640,7 @@ void ConvolutionState::process(const size_t samplesToDo, /* Calculate the frequency-domain response and add the relevant * frequency bins to the FFT history. */ - pffft_transform(mFft.get(), mInput.data(), mComplexData.data() + curseg*ConvolveUpdateSize, + mFft.transform(mInput.data(), mComplexData.data() + curseg*ConvolveUpdateSize, mFftWorkBuffer.data(), PFFFT_FORWARD); const float *filter{mComplexData.data() + mNumConvolveSegs*ConvolveUpdateSize}; @@ -655,14 +653,14 @@ void ConvolutionState::process(const size_t samplesToDo, const float *input{&mComplexData[curseg*ConvolveUpdateSize]}; for(size_t s{curseg};s < mNumConvolveSegs;++s) { - pffft_zconvolve_accumulate(mFft.get(), input, filter, mFftBuffer.data()); + mFft.zconvolve_accumulate(input, filter, mFftBuffer.data()); input += ConvolveUpdateSize; filter += ConvolveUpdateSize; } input = mComplexData.data(); for(size_t s{0};s < curseg;++s) { - pffft_zconvolve_accumulate(mFft.get(), input, filter, mFftBuffer.data()); + mFft.zconvolve_accumulate(input, filter, mFftBuffer.data()); input += ConvolveUpdateSize; filter += ConvolveUpdateSize; } @@ -672,8 +670,8 @@ void ConvolutionState::process(const size_t samplesToDo, * second-half samples (and this output's second half is * subsequently saved for next time). */ - pffft_transform(mFft.get(), mFftBuffer.data(), mFftBuffer.data(), - mFftWorkBuffer.data(), PFFFT_BACKWARD); + 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) |