/* Copyright 2018 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/contrib/lite/kernels/internal/mfcc_dct.h" #include namespace tflite { namespace internal { 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) { return false; } if (input_length < 1) { return false; } if (coefficient_count_ > 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 = 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 &input, std::vector *output) const { if (!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 internal } // namespace tflite