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added multi-description example with dataset and json output (#260)
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* added multi-description example with dataset and json output

* corrected readme

* example with UMAP in action

* Update flucoma-core for hisstools header-only

* Update hisstools library tag

* clang_format the relevant files

* Update HISSTools tags for out audio file fixes

* Update tag for hisstools_library for sample writing offset fix

* clang-formatted

---------

Co-authored-by: lewardo <[email protected]>
Co-authored-by: FluCoMa <[email protected]>
Co-authored-by: Alex Harker <[email protected]>
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4 people committed Mar 1, 2024
1 parent c4e6081 commit a3aa9ce
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Showing 4 changed files with 249 additions and 11 deletions.
16 changes: 6 additions & 10 deletions examples/CMakeLists.txt
Original file line number Diff line number Diff line change
@@ -1,13 +1,9 @@
foreach (EXAMPLE describe)
foreach (EXAMPLE dataset describe umap)

add_executable (
${EXAMPLE} ${EXAMPLE}.cpp
)
add_executable(${EXAMPLE} ${EXAMPLE}.cpp)

target_link_libraries(
${EXAMPLE} PRIVATE FLUID_DECOMPOSITION
)
target_link_libraries(${EXAMPLE} PRIVATE FLUID_DECOMPOSITION)

target_compile_options(${EXAMPLE} PRIVATE ${FLUID_ARCH})
endforeach (EXAMPLE)
target_compile_options(${EXAMPLE} PRIVATE ${FLUID_ARCH})

endforeach(EXAMPLE)
6 changes: 5 additions & 1 deletion examples/README.md
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Expand Up @@ -9,7 +9,11 @@ mkdir build && cd build
# CMake to configure targets
cmake .. -DBUILD_EXAMPLES=ON

# build example target, currently only `describe`
# build an example target, currently three options:
# 'describe' takes a single file in and prints stats on various descriptors
# 'dataset' takes a list of soundfiles and make an entry per file in a dataset saved as json
# 'umap' takes a multidimensional dataset input as json, and will use UMAP to reduce the dimension count to 2

cmake --build . --target describe
# OR
make describe
Expand Down
162 changes: 162 additions & 0 deletions examples/dataset.cpp
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@@ -0,0 +1,162 @@
/*
Part of the Fluid Corpus Manipulation Project (http://www.flucoma.org/)
Copyright University of Huddersfield.
Licensed under the BSD-3 License.
See license.md file in the project root for full license information.
This project has received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 725899).
*/

/*
This program demonstrates the use of the fluid decomposition toolbox
to produces a dataset with the summary of spectral features on files
*/

#include <Eigen/Core>
#include <algorithms/public/DCT.hpp>
#include <algorithms/public/Loudness.hpp>
#include <algorithms/public/MelBands.hpp>
#include <algorithms/public/MultiStats.hpp>
#include <algorithms/public/STFT.hpp>
#include <algorithms/public/SpectralShape.hpp>
#include <algorithms/public/YINFFT.hpp>
#include <audio_file/in_file.hpp>
#include <data/FluidDataSet.hpp>
#include <data/FluidIndex.hpp>
#include <data/FluidJSON.hpp>
#include <data/FluidMemory.hpp>
#include <data/TensorTypes.hpp>
#include <cstdio>
#include <iomanip>
#include <iostream>
#include <vector>

fluid::RealVector computeStats(fluid::RealMatrixView matrix,
fluid::algorithm::MultiStats stats)
{
fluid::index dim = matrix.cols();
fluid::RealMatrix tmp(dim, 7);
fluid::RealVector result(dim * 7);
stats.process(matrix.transpose(), tmp);
for (int j = 0; j < dim; j++)
{
result(fluid::Slice(j * 7, 7)) <<= tmp.row(j);
}
return result;
}

int main(int argc, char* argv[])
{
using namespace fluid;
using namespace fluid::algorithm;
using fluid::index;

if (argc <= 2)
{
std::cerr << "usage: describe output_file.json input_file_1.wav "
"input_file_2.wav...\n";
return 1;
}

FluidDataSet<std::string, double, 1> dataset(168);

const char* outputFile = argv[1];
for (int i = 2; i < argc; i++)
{
const char* inputFile = argv[i];
htl::in_audio_file file(inputFile);

index nSamples = file.frames();
auto samplingRate = file.sampling_rate();

if (!file.is_open())
{
std::cerr << "input file " << inputFile << " could not be opened\n";
return -2;
}

index nBins = 513;
index fftSize = 2 * (nBins - 1);
index hopSize = 1024;
index windowSize = 1024;
index halfWindow = windowSize / 2;
index nBands = 40;
index nCoefs = 13;
index minFreq = 20;
index maxFreq = 5000;

STFT stft{windowSize, fftSize, hopSize};
MelBands bands{nBands, fftSize};
DCT dct{nBands, nCoefs};
YINFFT yin{nBins, FluidDefaultAllocator()};
SpectralShape shape(FluidDefaultAllocator());
Loudness loudness{windowSize};
MultiStats stats;

bands.init(minFreq, maxFreq, nBands, nBins, samplingRate, windowSize);
dct.init(nBands, nCoefs);
stats.init(0, 0, 50, 100);
loudness.init(windowSize, samplingRate);

RealVector in(nSamples);
file.read_channel(in.data(), nSamples, 0);
RealVector padded(in.size() + windowSize + hopSize);
index nFrames = floor((padded.size() - windowSize) / hopSize);
RealMatrix pitchMat(nFrames, 2);
RealMatrix loudnessMat(nFrames, 2);
RealMatrix mfccMat(nFrames, nCoefs);
RealMatrix shapeMat(nFrames, 7);
std::fill(padded.begin(), padded.end(), 0);
padded(Slice(halfWindow, in.size())) <<= in;

for (int i = 0; i < nFrames; i++)
{
ComplexVector frame(nBins);
RealVector magnitude(nBins);
RealVector mels(nBands);
RealVector mfccs(nCoefs);
RealVector pitch(2);
RealVector shapeDesc(7);
RealVector loudnessDesc(2);
RealVectorView window = padded(fluid::Slice(i * hopSize, windowSize));
stft.processFrame(window, frame);
stft.magnitude(frame, magnitude);
bands.processFrame(magnitude, mels, false, false, true,
FluidDefaultAllocator());
dct.processFrame(mels, mfccs);
mfccMat.row(i) <<= mfccs;
yin.processFrame(magnitude, pitch, minFreq, maxFreq, samplingRate);
pitchMat.row(i) <<= pitch;
shape.processFrame(magnitude, shapeDesc, samplingRate, 0, -1, 0.95, false,
false, FluidDefaultAllocator());
shapeMat.row(i) <<= shapeDesc;
loudness.processFrame(window, loudnessDesc, true, true);
loudnessMat.row(i) <<= loudnessDesc;
}

RealVector pitchStats = computeStats(pitchMat, stats);
RealVector loudnessStats = computeStats(loudnessMat, stats);
RealVector shapeStats = computeStats(shapeMat, stats);
RealVector mfccStats = computeStats(mfccMat, stats);

RealVector allStats(168);

allStats(fluid::Slice(0, 14)) <<= pitchStats;
allStats(fluid::Slice(14, 14)) <<= loudnessStats;
allStats(fluid::Slice(28, 49)) <<= shapeStats;
allStats(fluid::Slice(77, 91)) <<= mfccStats;

dataset.add(inputFile, allStats);
}

auto outputJSON = JSONFile(outputFile, "w");
outputJSON.write(dataset);

if (!outputJSON.ok())
{
std::cerr << "failed to write output to " << outputFile << "\n";
}

return 0;
}
76 changes: 76 additions & 0 deletions examples/umap.cpp
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@@ -0,0 +1,76 @@
/*
Part of the Fluid Corpus Manipulation Project (http://www.flucoma.org/)
Copyright University of Huddersfield.
Licensed under the BSD-3 License.
See license.md file in the project root for full license information.
This project has received funding from the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation programme
(grant agreement No 725899).
*/

/*
This program demonstrates the use of the fluid decomposition toolbox
to apply an algorithm on an input dataset
*/

#include "algorithms/public/UMAP.hpp"
#include <Eigen/Core>
#include <data/FluidDataSet.hpp>
#include <data/FluidIndex.hpp>
#include <data/FluidJSON.hpp>
#include <data/FluidMemory.hpp>
#include <data/TensorTypes.hpp>
#include <cstdio>
#include <iomanip>
#include <iostream>
#include <vector>

int main(int argc, char* argv[])
{
using namespace fluid;
using namespace fluid::algorithm;
using fluid::index;

if (argc != 3)
{
std::cerr << "usage: umap input_file.json output_file.json\n";
return 1;
}

FluidDataSet<std::string, double, 1> datasetIN(1);
FluidDataSet<std::string, double, 1> datasetOUT(1);

const char* inputFile = argv[1];
const char* outputFile = argv[2];

auto inputJSON = JSONFile(inputFile, "r");
nlohmann::json j = inputJSON.read();

if (!inputJSON.ok())
{
std::cerr << "failed to read input " << inputFile << "\n";
return 2;
}

if (!check_json(j, datasetIN))
{
std::cerr << "Invalid JSON format\n";
return 3;
}

datasetIN = j.get<FluidDataSet<std::string, double, 1>>();

algorithm::UMAP algorithm;

datasetOUT = algorithm.train(datasetIN, 15, 2, 0.1, 200, 0.1);

auto outputJSON = JSONFile(outputFile, "w");
outputJSON.write(datasetOUT);

if (!outputJSON.ok())
{
std::cerr << "failed to write output to " << outputFile << "\n";
}

return 0;
}

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