EVOLUTION-MANAGER

Edit File: spectrogram.h

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// Class for generating spectrogram slices from a waveform.
// Initialize() should be called before calls to other functions.  Once
// Initialize() has been called and returned true, The Compute*() functions can
// be called repeatedly with sequential input data (ie. the first element of the
// next input vector directly follows the last element of the previous input
// vector). Whenever enough audio samples are buffered to produce a
// new frame, it will be placed in output. Output is cleared on each
// call to Compute*(). This class is thread-unsafe, and should only be
// called from one thread at a time.
// With the default parameters, the output of this class should be very
// close to the results of the following MATLAB code:
// overlap_samples = window_length_samples - step_samples;
// window = hann(window_length_samples, 'periodic');
// S = abs(spectrogram(audio, window, overlap_samples)).^2;

#ifndef TENSORFLOW_CORE_KERNELS_SPECTROGRAM_H_
#define TENSORFLOW_CORE_KERNELS_SPECTROGRAM_H_

#include <complex>
#include <deque>
#include <vector>

#include "third_party/fft2d/fft.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"

namespace tensorflow {

class Spectrogram {
 public:
  Spectrogram() : initialized_(false) {}
  ~Spectrogram() {}

// Initializes the class with a given window length and step length
  // (both in samples). Internally a Hann window is used as the window
  // function. Returns true on success, after which calls to Process()
  // are possible. window_length must be greater than 1 and step
  // length must be greater than 0.
  bool Initialize(int window_length, int step_length);

// Initialize with an explicit window instead of a length.
  bool Initialize(const std::vector<double>& window, int step_length);

// Reset internal variables.
  // Spectrogram keeps internal state: remaining input data from previous call.
  // As a result it can produce different number of frames when you call
  // ComputeComplexSpectrogram multiple times (even though input data
  // has the same size). As it is shown in
  // MultipleCallsToComputeComplexSpectrogramMayYieldDifferentNumbersOfFrames
  // in tensorflow/core/kernels/spectrogram_test.cc.
  // But if you need to compute Spectrogram on input data without keeping
  // internal state (and clear remaining input data from the previous call)
  // you have to call Reset() before computing Spectrogram.
  // For example in tensorflow/core/kernels/spectrogram_op.cc
  bool Reset();

// Processes an arbitrary amount of audio data (contained in input)
  // to yield complex spectrogram frames. After a successful call to
  // Initialize(), Process() may be called repeatedly with new input data
  // each time.  The audio input is buffered internally, and the output
  // vector is populated with as many temporally-ordered spectral slices
  // as it is possible to generate from the input.  The output is cleared
  // on each call before the new frames (if any) are added.
  //
  // The template parameters can be float or double.
  template <class InputSample, class OutputSample>
  bool ComputeComplexSpectrogram(
      const std::vector<InputSample>& input,
      std::vector<std::vector<std::complex<OutputSample>>>* output);

// This function works as the one above, but returns the power
  // (the L2 norm, or the squared magnitude) of each complex value.
  template <class InputSample, class OutputSample>
  bool ComputeSquaredMagnitudeSpectrogram(
      const std::vector<InputSample>& input,
      std::vector<std::vector<OutputSample>>* output);

// Return reference to the window function used internally.
  const std::vector<double>& GetWindow() const { return window_; }

// Return the number of frequency channels in the spectrogram.
  int output_frequency_channels() const { return output_frequency_channels_; }

private:
  template <class InputSample>
  bool GetNextWindowOfSamples(const std::vector<InputSample>& input,
                              int* input_start);
  void ProcessCoreFFT();

int fft_length_;
  int output_frequency_channels_;
  int window_length_;
  int step_length_;
  bool initialized_;
  int samples_to_next_step_;

std::vector<double> window_;
  std::vector<double> fft_input_output_;
  std::deque<double> input_queue_;

// Working data areas for the FFT routines.
  std::vector<int> fft_integer_working_area_;
  std::vector<double> fft_double_working_area_;

TF_DISALLOW_COPY_AND_ASSIGN(Spectrogram);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_SPECTROGRAM_H_