mfcc_dct.cc

/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/kernels/mfcc_dct.h"

#include <math.h>
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

MfccDct::MfccDct() : initialized_(false) {}

bool MfccDct::Initialize(int input_length, int coefficient_count) {
  coefficient_count_ = coefficient_count;
  input_length_ = input_length;

  if (coefficient_count_ < 1) {
    LOG(ERROR) << "Coefficient count must be positive.";
    return false;
  }

  if (input_length < 1) {
    LOG(ERROR) << "Input length must be positive.";
    return false;
  }

  if (coefficient_count_ > input_length_) {
    LOG(ERROR) << "Coefficient count must be less than or equal to "
               << "input length.";
    return false;
  }

  cosines_.resize(coefficient_count_);
  double fnorm = sqrt(2.0 / input_length_);
  // Some platforms don't have M_PI, so define a local constant here.
  const double pi = std::atan(1) * 4;
  double arg = pi / input_length_;
  for (int i = 0; i < coefficient_count_; ++i) {
    cosines_[i].resize(input_length_);
    for (int j = 0; j < input_length_; ++j) {
      cosines_[i][j] = fnorm * cos(i * arg * (j + 0.5));
    }
  }
  initialized_ = true;
  return true;
}

void MfccDct::Compute(const std::vector<double> &input,
                      std::vector<double> *output) const {
  if (!initialized_) {
    LOG(ERROR) << "DCT not initialized.";
    return;
  }

  output->resize(coefficient_count_);
  int length = input.size();
  if (length > input_length_) {
    length = input_length_;
  }

  for (int i = 0; i < coefficient_count_; ++i) {
    double sum = 0.0;
    for (int j = 0; j < length; ++j) {
      sum += cosines_[i][j] * input[j];
    }
    (*output)[i] = sum;
  }
}

}  // namespace tensorflow