Minor code refactor for the frequency shifter effect

[openal-soft.git] / alc / effects / convolution.cpp

#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(float *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_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};
    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_f,ConvolveUpdateSize> mFftBuffer{};

    size_t mCurrentSegment{0};
    size_t mNumConvolveSegs{0};

    struct ChannelData {
        alignas(16) FloatBufferLine mBuffer{};
        float mHfScale{}, mLfScale{};
        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_f[]> 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.processScale(src, chan.mHfScale, chan.mLfScale);
        MixSamples(src, samplesOut, chan.Current, chan.Target, samplesToDo, 0);
    }
}


void ConvolutionState::deviceUpdate(const DeviceBase *device, const Buffer &buffer)
{
    using UhjDecoderType = UhjDecoder<512>;
    static constexpr auto DecoderPadding = UhjDecoderType::sInputPadding;

    constexpr uint MaxConvolveAmbiOrder{1u};

    mFifoPos = 0;
    mInput.fill(0.0f);
    decltype(mFilter){}.swap(mFilter);
    decltype(mOutput){}.swap(mOutput);
    mFftBuffer.fill(complex_f{});

    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;

    mChannels = buffer.storage->mChannels;
    mAmbiLayout = IsUHJ(mChannels) ? AmbiLayout::FuMa : buffer.storage->mAmbiLayout;
    mAmbiScaling = IsUHJ(mChannels) ? AmbiScaling::UHJ : buffer.storage->mAmbiScaling;
    mAmbiOrder = minu(buffer.storage->mAmbiOrder, MaxConvolveAmbiOrder);

    constexpr size_t m{ConvolveUpdateSize/2 + 1};
    const auto bytesPerSample = BytesFromFmt(buffer.storage->mType);
    const auto realChannels = buffer.storage->channelsFromFmt();
    const auto numChannels = (mChannels == FmtUHJ2) ? 3u : ChannelsFromFmt(mChannels, mAmbiOrder);

    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
     * 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.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_f[]>(complex_length);
    std::fill_n(mComplexData.get(), complex_length, complex_f{});

    /* Load the samples from the buffer. */
    const size_t srclinelength{RoundUp(buffer.storage->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.samples.data() + bytesPerSample*c,
            realChannels, buffer.storage->mType, buffer.storage->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.storage->mSampleLen,
            buffer.storage->mSampleLen);
    }

    auto ressamples = std::make_unique<double[]>(buffer.storage->mSampleLen +
        (resampler ? resampledCount : 0));
    complex_f *filteriter = mComplexData.get() + mNumConvolveSegs*m;
    for(size_t c{0};c < numChannels;++c)
    {
        /* Resample to match the device. */
        if(resampler)
        {
            std::copy_n(srcsamples.get() + srclinelength*c, buffer.storage->mSampleLen,
                ressamples.get() + resampledCount);
            resampler.process(buffer.storage->mSampleLen, ressamples.get()+resampledCount,
                resampledCount, ressamples.get());
        }
        else
            std::copy_n(srcsamples.get() + srclinelength*c, buffer.storage->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); });

        auto fftbuffer = std::vector<std::complex<double>>(ConvolveUpdateSize);
        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(&ressamples[done], todo, fftbuffer.begin());
            done += todo;
            std::fill(iter, fftbuffer.end(), std::complex<double>{});

            forward_fft(al::as_span(fftbuffer));
            filteriter = std::copy_n(fftbuffer.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) [[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;

        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]};
            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) [[unlikely]]
        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({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_f{});
        forward_fft(al::as_span(mFftBuffer));

        std::copy_n(mFftBuffer.cbegin(), m, &mComplexData[curseg*m]);

        const complex_f *RESTRICT filter{mComplexData.get() + mNumConvolveSegs*m};
        for(size_t c{0};c < chans.size();++c)
        {
            std::fill_n(mFftBuffer.begin(), m, complex_f{});

            /* Convolve each input segment with its IR filter counterpart
             * (aligned in time).
             */
            const complex_f *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(al::as_span(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] =
                    (mFftBuffer[i].real()+mOutput[c][ConvolveUpdateSamples+i]) *
                    (1.0f/float{ConvolveUpdateSize});
            for(size_t i{0};i < ConvolveUpdateSamples;++i)
                mOutput[c][ConvolveUpdateSamples+i] = mFftBuffer[ConvolveUpdateSamples+i].real();
        }

        /* 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;
}