1/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
2
3Licensed under the Apache License, Version 2.0 (the "License");
4you may not use this file except in compliance with the License.
5You may obtain a copy of the License at
6
7 http://www.apache.org/licenses/LICENSE-2.0
8
9Unless required by applicable law or agreed to in writing, software
10distributed under the License is distributed on an "AS IS" BASIS,
11WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12See the License for the specific language governing permissions and
13limitations under the License.
14==============================================================================*/
15
16#include "tensorflow/core/kernels/mfcc_dct.h"
17
18#include <math.h>
19#include "tensorflow/core/platform/logging.h"
20
21namespace tensorflow {
22
23MfccDct::MfccDct() : initialized_(false) {}
24
25bool MfccDct::Initialize(int input_length, int coefficient_count) {
26 coefficient_count_ = coefficient_count;
27 input_length_ = input_length;
28
29 if (coefficient_count_ < 1) {
30 LOG(ERROR) << "Coefficient count must be positive.";
31 return false;
32 }
33
34 if (input_length < 1) {
35 LOG(ERROR) << "Input length must be positive.";
36 return false;
37 }
38
39 if (coefficient_count_ > input_length_) {
40 LOG(ERROR) << "Coefficient count must be less than or equal to "
41 << "input length.";
42 return false;
43 }
44
45 cosines_.resize(coefficient_count_);
46 double fnorm = sqrt(2.0 / input_length_);
47 // Some platforms don't have M_PI, so define a local constant here.
48 const double pi = std::atan(1) * 4;
49 double arg = pi / input_length_;
50 for (int i = 0; i < coefficient_count_; ++i) {
51 cosines_[i].resize(input_length_);
52 for (int j = 0; j < input_length_; ++j) {
53 cosines_[i][j] = fnorm * cos(i * arg * (j + 0.5));
54 }
55 }
56 initialized_ = true;
57 return true;
58}
59
60void MfccDct::Compute(const std::vector<double> &input,
61 std::vector<double> *output) const {
62 if (!initialized_) {
63 LOG(ERROR) << "DCT not initialized.";
64 return;
65 }
66
67 output->resize(coefficient_count_);
68 int length = input.size();
69 if (length > input_length_) {
70 length = input_length_;
71 }
72
73 for (int i = 0; i < coefficient_count_; ++i) {
74 double sum = 0.0;
75 for (int j = 0; j < length; ++j) {
76 sum += cosines_[i][j] * input[j];
77 }
78 (*output)[i] = sum;
79 }
80}
81
82} // namespace tensorflow
83