544 lines
18 KiB
C++
544 lines
18 KiB
C++
#ifndef SIGNALSMITH_STRETCH_H
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#define SIGNALSMITH_STRETCH_H
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#include "dsp/spectral.h"
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#include "dsp/delay.h"
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#include "dsp/curves.h"
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#include <vector>
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#include <algorithm>
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namespace signalsmith { namespace stretch {
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template<typename Sample=float>
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struct SignalsmithStretch {
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int blockSamples() const {
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return stft.windowSize();
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}
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int intervalSamples() const {
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return stft.interval();
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}
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int inputLatency() const {
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return stft.windowSize()/2;
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}
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int outputLatency() const {
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return stft.windowSize() - inputLatency();
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}
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void reset() {
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stft.reset();
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inputBuffer.reset();
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prevInputOffset = -1;
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channelBands.assign(channelBands.size(), Band());
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}
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/// Configures using a default preset
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void presetDefault(int nChannels, Sample sampleRate) {
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configure(nChannels, sampleRate*0.12, sampleRate*0.03);
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freqWeight = 1;
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timeWeight = 2;
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channelWeight = 0.5;
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}
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// manual parameters
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Sample freqWeight = 1, timeWeight = 2, channelWeight = 0.5, maxWeight = 2;
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bool sortOrder = true; // Assemble output spectrum highest-magnitude first
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/// Manual setup
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void configure(int nChannels, int blockSamples, int intervalSamples) {
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channels = nChannels;
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stft.resize(channels, blockSamples, intervalSamples);
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inputBuffer.resize(channels, blockSamples);
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timeBuffer.assign(stft.fftSize(), 0);
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channelBands.assign(stft.bands()*channels, Band());
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// Various phase rotations
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rotCentreSpectrum.resize(stft.bands());
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rotPrevInput.assign(stft.bands(), 0);
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rotPrevOutput.resize(stft.bands());
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timeShiftPhases(blockSamples*Sample(-0.5), rotCentreSpectrum);
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timeShiftPhases(-intervalSamples, rotPrevOutput);
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peaks.reserve(stft.bands());
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energy.resize(stft.bands());
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smoothedEnergy.resize(stft.bands());
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outputMap.resize(stft.bands());
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channelPredictions.resize(channels*stft.bands());
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maxEnergyChannel.resize(stft.bands());
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}
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template<class Inputs, class Outputs>
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void process(Inputs &&inputs, int inputSamples, Outputs &&outputs, int outputSamples) {
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Sample timeScaling = Sample(inputSamples)/outputSamples;
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for (int outputIndex = 0; outputIndex < outputSamples; ++outputIndex) {
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stft.ensureValid(outputIndex, [&](int outputOffset) {
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// Time to process a spectrum! Where should it come from in the input?
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int inputOffset = (outputOffset*inputSamples)/outputSamples - stft.windowSize();
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int inputInterval = inputOffset - prevInputOffset;
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prevInputOffset = inputOffset;
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if (inputInterval > 0) {
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timeShiftPhases(-inputInterval, rotPrevInput);
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for (int c = 0; c < channels; ++c) {
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// Copy from the history buffer, if needed
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auto &&bufferChannel = inputBuffer[c];
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for (int i = 0; i < -inputOffset; ++i) {
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timeBuffer[i] = bufferChannel[i + inputOffset];
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}
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// Copy the rest from the input
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auto &&inputChannel = inputs[c];
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for (int i = std::max<int>(0, -inputOffset); i < stft.windowSize(); ++i) {
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timeBuffer[i] = inputChannel[i + inputOffset];
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}
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stft.analyse(c, timeBuffer);
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}
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}
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for (int c = 0; c < channels; ++c) {
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auto bands = bandsForChannel(c);
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auto &&spectrumBands = stft.spectrum[c];
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for (int b = 0; b < stft.bands(); ++b) {
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bands[b].input = spectrumBands[b]*rotCentreSpectrum[b];
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}
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}
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processSpectrum(inputInterval);
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for (int c = 0; c < channels; ++c) {
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auto bands = bandsForChannel(c);
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auto &&spectrumBands = stft.spectrum[c];
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for (int b = 0; b < stft.bands(); ++b) {
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spectrumBands[b] = bands[b].output*std::conj(rotCentreSpectrum[b]);
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}
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}
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});
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for (int c = 0; c < channels; ++c) {
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auto &&outputChannel = outputs[c];
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auto &&stftChannel = stft[c];
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outputChannel[outputIndex] = stftChannel[outputIndex];
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}
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}
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// Store input in history buffer
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for (int c = 0; c < channels; ++c) {
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auto &&inputChannel = inputs[c];
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auto &&bufferChannel = inputBuffer[c];
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int startIndex = std::max<int>(0, inputSamples - stft.windowSize());
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for (int i = startIndex; i < inputSamples; ++i) {
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bufferChannel[i] = inputChannel[i];
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}
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}
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inputBuffer += inputSamples;
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stft += outputSamples;
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prevInputOffset -= inputSamples;
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}
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/// Frequency multiplier, and optional tonality limit (as multiple of sample-rate)
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void setTransposeFactor(Sample multiplier, Sample tonalityLimit=0) {
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freqMultiplier = multiplier;
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if (tonalityLimit > 0) {
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freqTonalityLimit = tonalityLimit/std::sqrt(multiplier); // compromise between input and output limits
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} else {
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freqTonalityLimit = 1;
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}
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}
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void setTransposeSemitones(Sample semitones, Sample tonalityLimit=0) {
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setTransposeFactor(std::pow(2, semitones/12), tonalityLimit);
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}
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private:
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using Complex = std::complex<Sample>;
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Sample freqMultiplier = 1, freqTonalityLimit = 0.5;
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signalsmith::spectral::STFT<Sample> stft{0, 1, 1};
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signalsmith::delay::MultiBuffer<Sample> inputBuffer;
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int channels = 0;
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int prevInputOffset = -1;
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std::vector<Sample> timeBuffer;
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std::vector<Complex> rotCentreSpectrum, rotPrevOutput, rotPrevInput;
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Sample bandToFreq(int b) const {
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return (b + Sample(0.5))/stft.fftSize();
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}
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void timeShiftPhases(Sample shiftSamples, std::vector<Complex> &output) const {
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for (int b = 0; b < stft.bands(); ++b) {
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Sample phase = bandToFreq(b)*shiftSamples*Sample(-2*M_PI);
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output[b] = {std::cos(phase), std::sin(phase)};
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}
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}
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struct Band {
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Complex input, prevInput{0};
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Complex output, prevOutput{0};
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};
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std::vector<Band> channelBands;
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Band * bandsForChannel(int channel) {
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return channelBands.data() + channel*stft.bands();
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}
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template<Complex Band::*member>
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Complex getBand(int channel, int index) {
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if (index >= stft.bands()) return 0;
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if (index < 0) {
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return std::conj(getBand<member>(channel, -1 - index));
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}
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return channelBands[index + channel*stft.bands()].*member;
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}
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template<Complex Band::*member>
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Complex getFractional(int channel, int lowIndex, Sample fractional) {
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Complex low = getBand<member>(channel, lowIndex);
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Complex high = getBand<member>(channel, lowIndex + 1);
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return low + (high - low)*fractional;
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}
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template<Complex Band::*member>
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Complex getFractional(int channel, Sample inputIndex) {
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int lowIndex = std::floor(inputIndex);
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Sample fracIndex = inputIndex - std::floor(inputIndex);
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return getFractional<member>(channel, lowIndex, fracIndex);
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}
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template<Sample Band::*member>
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Sample getBand(int channel, int index) {
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if (index < 0) index = -1 - index;
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if (index >= stft.bands()) return 0;
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return channelBands[index + channel*stft.bands()].*member;
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}
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template<Sample Band::*member>
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Sample getFractional(int channel, int lowIndex, Sample fractional) {
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Sample low = getBand<member>(channel, lowIndex);
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Sample high = getBand<member>(channel, lowIndex + 1);
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return low + (high - low)*fractional;
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}
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template<Sample Band::*member>
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Sample getFractional(int channel, Sample inputIndex) {
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int lowIndex = std::floor(inputIndex);
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Sample fracIndex = inputIndex - std::floor(inputIndex);
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return getFractional<member>(channel, lowIndex, fracIndex);
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}
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struct Peak {
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Sample input, output, energy;
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bool operator< (const Peak &other) const {
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return output < other.output;
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}
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};
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std::vector<Peak> peaks;
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std::vector<Sample> energy, smoothedEnergy;
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struct PitchMapPoint {
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Sample inputBin, freqGrad;
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};
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std::vector<PitchMapPoint> outputMap;
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struct Prediction {
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Sample energy;
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Complex input;
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Complex freqPrediction;
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Complex shortVerticalTwist, longVerticalTwist;
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Complex channelTwist;
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};
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std::vector<Prediction> channelPredictions;
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Prediction * predictionsForChannel(int c) {
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return channelPredictions.data() + c*stft.bands();
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}
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std::vector<int> maxEnergyChannel;
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void processSpectrum(int inputInterval) {
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int outputInterval = stft.interval();
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int bands = stft.bands();
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Sample rate = Sample(inputInterval)/outputInterval;
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if (inputInterval > 0) {
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for (int c = 0; c < channels; ++c) {
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auto bins = bandsForChannel(c);
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for (int b = 0; b < stft.bands(); ++b) {
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auto &bin = bins[b];
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bins[b].prevOutput *= rotPrevOutput[b];
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bins[b].prevInput *= rotPrevInput[b];
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}
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}
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}
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Sample smoothingBins = Sample(stft.fftSize())/stft.interval();
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findPeaks(smoothingBins);
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updateOutputMap(smoothingBins);
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int longVerticalStep = std::round(smoothingBins);
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auto *predictions0 = predictionsForChannel(0);
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for (auto &c : maxEnergyChannel) c = -1;
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for (int c = 0; c < channels; ++c) {
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Band *bins = bandsForChannel(c);
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auto *predictions = predictionsForChannel(c);
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for (int b = 0; b < stft.bands(); ++b) {
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auto &outputBin = bins[b];
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auto mapPoint = outputMap[b];
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int lowIndex = std::floor(mapPoint.inputBin);
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Sample fracIndex = mapPoint.inputBin - std::floor(mapPoint.inputBin);
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Prediction prediction;
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prediction.energy = getFractional<&Band::energy>(c, lowIndex, fracIndex);
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prediction.energy *= std::max<Sample>(0, mapPoint.freqGrad); // scale the energy according to local stretch factor
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prediction.input = getFractional<&Band::input>(c, lowIndex, fracIndex);
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Complex prevInput = getFractional<&Band::prevInput>(c, lowIndex, fracIndex);
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Complex freqTwist = prediction.input*std::conj(prevInput);
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prediction.freqPrediction = outputBin.prevOutput*freqTwist;
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if (c > 0) {
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prediction.channelTwist = prediction.input*std::conj(predictions0[b]);
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} else {
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prediction.channelTwist = 0;
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}
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if (b > 0) {
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Complex downInput = getFractional<&Band::input>(c, mapPoint.inputBin - rate);
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prediction.shortVerticalTwist = prediction.input*std::conj(downInput);
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if (b > longVerticalStep) {
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Complex longDownInput = getFractional<&Band::input>(c, mapPoint.inputBin - longVerticalStep*rate);
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prediction.longVerticalTwist = prediction.input*std::conj(longDownInput);
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} else {
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prediction.longVerticalTwist = 0;
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}
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} else {
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prediction.shortVerticalTwist = prediction.longVerticalTwist = 0;
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}
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predictions[b] = prediction;
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// Rough output prediction based on phase-vocoder, sensitive to previous input/output magnitude
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outputBin.output = prediction.freqPrediction/(prediction.energy + 1e-10);
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}
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}
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for (int b = 0; b < stft.bands(); ++b) {
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int maxChannel = 0;
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maxEnergyChannel[b] = 0;
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Sample maxEnergy = predictions0[b].energy;
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for (int c = 1; c < channels; ++c) {
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Sample e = predictionsForChannel(c)[b].energy;
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if (e > maxEnergy) {
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maxChannel = c;
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maxEnergy = e;
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}
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}
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maxEnergyChannel[b] = maxChannel;
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Sample channelInput = predictionsForChannel(maxChannel)
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for (int c = 0; c < channels; ++c) {
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Prediction &prediction = predictionsForChannel(c)[b];
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if (c == maxChannel) {
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prediction.channelTwist = 0;
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} else {
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prediction.channelTwist = prediction.input*std::conj(channelInput);
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}
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}
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}
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for (auto &c : maxEnergyChannel) c = -1;
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for (auto &ordered : observationOrder) {
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auto *bins = bandsForChannel(ordered.channel);
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auto &outputBin = bins[ordered.outputBand];
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int lowIndex = std::floor(ordered.inputIndex);
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Sample fracIndex = ordered.inputIndex - std::floor(ordered.inputIndex);
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// We always have the phase-vocoder prediction
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Complex timeChange = ordered.input*std::conj(prevInput);
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Complex freqPrediction = outputBin.prevOutput*timeChange;
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Complex prediction = freqPrediction*freqWeight;
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// Track the strongest prediction
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Complex maxPrediction = freqPrediction;
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Sample maxPredictionNorm = std::norm(maxPrediction);
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// vertical upwards, if it exists
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if (ordered.outputBand > 0) {
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auto &outputDownBin = bins[ordered.outputBand - 1];
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if (outputDownBin.ready) {
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Complex downInput = getFractional<&Band::input>(ordered.channel, ordered.inputIndex - rate);
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Complex freqChange = ordered.input*std::conj(downInput);
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Complex newPrediction = outputDownBin.output*freqChange;
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prediction += newPrediction*timeWeight;
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if (std::norm(newPrediction) > maxPredictionNorm) {
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maxPredictionNorm = std::norm(newPrediction);
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maxPrediction = newPrediction;
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}
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}
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}
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// vertical downwards, if it exists
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if (ordered.outputBand < stft.bands() - 1) {
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auto &outputDownBin = bins[ordered.outputBand + 1];
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if (outputDownBin.ready) {
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Complex downInput = getFractional<&Band::input>(ordered.channel, ordered.inputIndex + rate);
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Complex freqChange = ordered.input*std::conj(downInput);
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Complex newPrediction = outputDownBin.output*freqChange;
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prediction += newPrediction*timeWeight;
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if (std::norm(newPrediction) > maxPredictionNorm) {
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maxPredictionNorm = std::norm(newPrediction);
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maxPrediction = newPrediction;
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}
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}
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}
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// longer verticals
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if (ordered.outputBand > longStep) {
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auto &outputDownBin = bins[ordered.outputBand - longStep];
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if (outputDownBin.ready) {
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Complex downInput = getFractional<&Band::input>(ordered.channel, ordered.inputIndex - longStep*rate);
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Complex freqChange = ordered.input*std::conj(downInput);
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Complex newPrediction = outputDownBin.output*freqChange;
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prediction += newPrediction*timeWeight;
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if (std::norm(newPrediction) > maxPredictionNorm) {
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maxPredictionNorm = std::norm(newPrediction);
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maxPrediction = newPrediction;
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}
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}
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}
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if (ordered.outputBand < stft.bands() - longStep) {
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auto &outputDownBin = bins[ordered.outputBand + longStep];
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if (outputDownBin.ready) {
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Complex downInput = getFractional<&Band::input>(ordered.channel, ordered.inputIndex + longStep*rate);
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Complex freqChange = ordered.input*std::conj(downInput);
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Complex newPrediction = outputDownBin.output*freqChange;
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prediction += newPrediction*timeWeight;
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if (std::norm(newPrediction) > maxPredictionNorm) {
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maxPredictionNorm = std::norm(newPrediction);
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maxPrediction = newPrediction;
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}
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}
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}
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// Inter-channel prediction, if it exists
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int &maxChannel = maxEnergyChannel[ordered.outputBand];
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if (maxChannel >= 0) {
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Complex otherInput = getFractional<&Band::input>(maxChannel, lowIndex, fracIndex);
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Complex channelRot = ordered.input*std::conj(otherInput);
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auto *otherBins = bandsForChannel(maxChannel);
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Complex otherOutputOutput = otherBins[ordered.outputBand].output;
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Complex newPrediction = otherOutputOutput*channelRot;
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prediction += newPrediction*channelWeight;
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if (std::norm(newPrediction) > maxPredictionNorm) {
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maxPredictionNorm = std::norm(newPrediction);
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maxPrediction = newPrediction;
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}
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} else {
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maxChannel = ordered.channel;
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}
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prediction += maxPrediction*maxWeight;
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Sample predictionNorm = std::norm(prediction);
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if (predictionNorm > 1e-15) {
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outputBin.output = prediction*std::sqrt(ordered.energy/predictionNorm);
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} else {
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outputBin.output = ordered.input;
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}
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}
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for (auto &bin : channelBands) {
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bin.prevOutput = bin.output;
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bin.prevInput = bin.input;
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}
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}
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// Produces smoothed energy across all channels
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void smoothEnergy(Sample smoothingBins) {
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Sample smoothingSlew = 1/(1 + smoothingBins*Sample(0.5));
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for (auto &e : energy) e = 0;
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for (int c = 0; c < channels; ++c) {
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Band *bins = bandsForChannel(c);
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for (int b = 0; b < stft.bands(); ++b) {
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Sample e = std::norm(bins[b].input);
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energy[b] += e;
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}
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}
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for (int b = 0; b < stft.bands(); ++b) {
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smoothedEnergy[b] = energy[b];
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}
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Sample e = 0;
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for (int repeat = 0; repeat < 2; ++repeat) {
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for (int b = stft.bands() - 1; b >= 0; --b) {
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e += (smoothedEnergy[b] - e)*smoothingSlew;
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smoothedEnergy[b] = e;
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}
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for (int b = 0; b < stft.bands(); ++b) {
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e += (smoothedEnergy[b] - e)*smoothingSlew;
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smoothedEnergy[b] = e;
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}
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}
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}
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Sample defaultFreqMap(Sample freq) const {
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if (freq > freqTonalityLimit) {
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Sample diff = freq - freqTonalityLimit;
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return freqTonalityLimit*freqMultiplier + diff;
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}
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return freq*freqMultiplier;
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}
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// Identifies spectral peaks using energy across all channels
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void findPeaks(Sample smoothingBins) {
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smoothEnergy(smoothingBins);
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peaks.resize(0);
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int start = 0;
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while (start < stft.bands()) {
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if (energy[start] > smoothedEnergy[start]) {
|
|
int end = start + 1;
|
|
while (end < stft.bands() && energy[end] > smoothedEnergy[end]) {
|
|
++end;
|
|
}
|
|
// Take the average frequency and energy across the peak range
|
|
Sample freqSum = 0, energySum = 0;
|
|
for (int b = start; b < end; ++b) {
|
|
Sample e = energy[b];
|
|
freqSum += (b + 0.5)*e;
|
|
energySum += e;
|
|
}
|
|
Sample avgFreq = freqSum/(stft.fftSize()*energySum);
|
|
Sample avgEnergy = energySum/(end - start);
|
|
peaks.emplace_back(Peak{avgFreq*stft.fftSize(), defaultFreqMap(avgFreq)*stft.fftSize(), avgEnergy});
|
|
|
|
start = end;
|
|
}
|
|
++start;
|
|
}
|
|
}
|
|
|
|
void updateOutputMap(Sample peakWidthBins) {
|
|
Sample linearZoneBins = peakWidthBins*Sample(0.5);
|
|
Sample bottomOffset = peaks[0].input - peaks[0].output;
|
|
for (int b = 0; b < std::min<int>(stft.bands(), peaks[0].output); ++b) {
|
|
outputMap[b] = {b + bottomOffset, 1};
|
|
}
|
|
for (size_t p = 1; p < peaks.size(); ++p) {
|
|
const Peak &prev = peaks[p - 1], &next = peaks[p];
|
|
Sample prevEnd = prev.output + linearZoneBins;
|
|
Sample nextStart = next.output - linearZoneBins;
|
|
if (nextStart < prevEnd) nextStart = prevEnd = (nextStart + prevEnd)*Sample(0.5);
|
|
signalsmith::curves::Linear<Sample> segment(prevEnd, nextStart, prev.input + linearZoneBins, next.input - linearZoneBins);
|
|
Sample segmentGrad = ((prev.input + linearZoneBins) - (next.input - linearZoneBins))/(prevEnd - nextStart + Sample(1e-10));
|
|
|
|
prevEnd = std::max<Sample>(0, std::min<Sample>(stft.bands(), prevEnd));
|
|
nextStart = std::max<Sample>(0, std::min<Sample>(stft.bands(), nextStart));
|
|
|
|
for (int b = std::max<int>(0, std::ceil(prev.output)); b < prevEnd; ++b) {
|
|
outputMap[b] = {b + prev.input - prev.output, 1};
|
|
}
|
|
for (int b = std::ceil(prevEnd); b < nextStart; ++b) {
|
|
outputMap[b] = {segment(b), segmentGrad};
|
|
}
|
|
for (int b = std::ceil(nextStart); b < std::min<int>(stft.bands(), std::ceil(next.output)); ++b) {
|
|
outputMap[b] = {b + next.input - next.output, 1};
|
|
}
|
|
}
|
|
Sample topOffset = peaks.back().input - peaks.back().output;
|
|
for (int b = std::max<int>(0, peaks.back().output); b < stft.bands(); ++b) {
|
|
outputMap[b] = {b + topOffset, 1};
|
|
}
|
|
}
|
|
};
|
|
|
|
}} // namespace
|
|
#endif // include guard
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