feature-functions.cc
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// feat/feature-functions.cc
// Copyright 2009-2011 Karel Vesely; Petr Motlicek; Microsoft Corporation
// 2013 Johns Hopkins University (author: Daniel Povey)
// 2014 IMSL, PKU-HKUST (author: Wei Shi)
// See ../../COPYING for clarification regarding multiple authors
//
// 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
//
// THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
// WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
// MERCHANTABLITY OR NON-INFRINGEMENT.
// See the Apache 2 License for the specific language governing permissions and
// limitations under the License.
#include "feat/feature-functions.h"
#include "matrix/matrix-functions.h"
namespace kaldi {
void ComputePowerSpectrum(VectorBase<BaseFloat> *waveform) {
int32 dim = waveform->Dim();
// no, letting it be non-power-of-two for now.
// KALDI_ASSERT(dim > 0 && (dim & (dim-1) == 0)); // make sure a power of two.. actually my FFT code
// does not require this (dan) but this is better in case we use different code [dan].
// RealFft(waveform, true); // true == forward (not inverse) FFT; makes no difference here,
// as we just want power spectrum.
// now we have in waveform, first half of complex spectrum
// it's stored as [real0, realN/2-1, real1, im1, real2, im2, ...]
int32 half_dim = dim/2;
BaseFloat first_energy = (*waveform)(0) * (*waveform)(0),
last_energy = (*waveform)(1) * (*waveform)(1); // handle this special case
for (int32 i = 1; i < half_dim; i++) {
BaseFloat real = (*waveform)(i*2), im = (*waveform)(i*2 + 1);
(*waveform)(i) = real*real + im*im;
}
(*waveform)(0) = first_energy;
(*waveform)(half_dim) = last_energy; // Will actually never be used, and anyway
// if the signal has been bandlimited sensibly this should be zero.
}
DeltaFeatures::DeltaFeatures(const DeltaFeaturesOptions &opts): opts_(opts) {
KALDI_ASSERT(opts.order >= 0 && opts.order < 1000); // just make sure we don't get binary junk.
// opts will normally be 2 or 3.
KALDI_ASSERT(opts.window > 0 && opts.window < 1000); // again, basic sanity check.
// normally the window size will be two.
scales_.resize(opts.order+1);
scales_[0].Resize(1);
scales_[0](0) = 1.0; // trivial window for 0th order delta [i.e. baseline feats]
for (int32 i = 1; i <= opts.order; i++) {
Vector<BaseFloat> &prev_scales = scales_[i-1],
&cur_scales = scales_[i];
int32 window = opts.window; // this code is designed to still
// work if instead we later make it an array and do opts.window[i-1],
// or something like that. "window" is a parameter specifying delta-window
// width which is actually 2*window + 1.
KALDI_ASSERT(window != 0);
int32 prev_offset = (static_cast<int32>(prev_scales.Dim()-1))/2,
cur_offset = prev_offset + window;
cur_scales.Resize(prev_scales.Dim() + 2*window); // also zeros it.
BaseFloat normalizer = 0.0;
for (int32 j = -window; j <= window; j++) {
normalizer += j*j;
for (int32 k = -prev_offset; k <= prev_offset; k++) {
cur_scales(j+k+cur_offset) +=
static_cast<BaseFloat>(j) * prev_scales(k+prev_offset);
}
}
cur_scales.Scale(1.0 / normalizer);
}
}
void DeltaFeatures::Process(const MatrixBase<BaseFloat> &input_feats,
int32 frame,
VectorBase<BaseFloat> *output_frame) const {
KALDI_ASSERT(frame < input_feats.NumRows());
int32 num_frames = input_feats.NumRows(),
feat_dim = input_feats.NumCols();
KALDI_ASSERT(static_cast<int32>(output_frame->Dim()) == feat_dim * (opts_.order+1));
output_frame->SetZero();
for (int32 i = 0; i <= opts_.order; i++) {
const Vector<BaseFloat> &scales = scales_[i];
int32 max_offset = (scales.Dim() - 1) / 2;
SubVector<BaseFloat> output(*output_frame, i*feat_dim, feat_dim);
for (int32 j = -max_offset; j <= max_offset; j++) {
// if asked to read
int32 offset_frame = frame + j;
if (offset_frame < 0) offset_frame = 0;
else if (offset_frame >= num_frames)
offset_frame = num_frames - 1;
BaseFloat scale = scales(j + max_offset);
if (scale != 0.0)
output.AddVec(scale, input_feats.Row(offset_frame));
}
}
}
ShiftedDeltaFeatures::ShiftedDeltaFeatures(
const ShiftedDeltaFeaturesOptions &opts): opts_(opts) {
KALDI_ASSERT(opts.window > 0 && opts.window < 1000);
// Default window is 1.
int32 window = opts.window;
KALDI_ASSERT(window != 0);
scales_.Resize(1 + 2*window); // also zeros it.
BaseFloat normalizer = 0.0;
for (int32 j = -window; j <= window; j++) {
normalizer += j*j;
scales_(j + window) += static_cast<BaseFloat>(j);
}
scales_.Scale(1.0 / normalizer);
}
void ShiftedDeltaFeatures::Process(const MatrixBase<BaseFloat> &input_feats,
int32 frame,
SubVector<BaseFloat> *output_frame) const {
KALDI_ASSERT(frame < input_feats.NumRows());
int32 num_frames = input_feats.NumRows(),
feat_dim = input_feats.NumCols();
KALDI_ASSERT(static_cast<int32>(output_frame->Dim())
== feat_dim * (opts_.num_blocks + 1));
output_frame->SetZero();
// The original features
SubVector<BaseFloat> output(*output_frame, 0, feat_dim);
output.AddVec(1.0, input_feats.Row(frame));
// Concatenate the delta-blocks. Each block is block_shift
// (usually 3) frames apart.
for (int32 i = 0; i < opts_.num_blocks; i++) {
int32 max_offset = (scales_.Dim() - 1) / 2;
SubVector<BaseFloat> output(*output_frame, (i + 1) * feat_dim, feat_dim);
for (int32 j = -max_offset; j <= max_offset; j++) {
int32 offset_frame = frame + j + i * opts_.block_shift;
if (offset_frame < 0) offset_frame = 0;
else if (offset_frame >= num_frames)
offset_frame = num_frames - 1;
BaseFloat scale = scales_(j + max_offset);
if (scale != 0.0)
output.AddVec(scale, input_feats.Row(offset_frame));
}
}
}
void ComputeDeltas(const DeltaFeaturesOptions &delta_opts,
const MatrixBase<BaseFloat> &input_features,
Matrix<BaseFloat> *output_features) {
output_features->Resize(input_features.NumRows(),
input_features.NumCols()
*(delta_opts.order + 1));
DeltaFeatures delta(delta_opts);
for (int32 r = 0; r < static_cast<int32>(input_features.NumRows()); r++) {
SubVector<BaseFloat> row(*output_features, r);
delta.Process(input_features, r, &row);
}
}
void ComputeShiftedDeltas(const ShiftedDeltaFeaturesOptions &delta_opts,
const MatrixBase<BaseFloat> &input_features,
Matrix<BaseFloat> *output_features) {
output_features->Resize(input_features.NumRows(),
input_features.NumCols()
* (delta_opts.num_blocks + 1));
ShiftedDeltaFeatures delta(delta_opts);
for (int32 r = 0; r < static_cast<int32>(input_features.NumRows()); r++) {
SubVector<BaseFloat> row(*output_features, r);
delta.Process(input_features, r, &row);
}
}
void InitIdftBases(int32 n_bases, int32 dimension, Matrix<BaseFloat> *mat_out) {
BaseFloat angle = M_PI / static_cast<BaseFloat>(dimension - 1);
BaseFloat scale = 1.0f / (2.0 * static_cast<BaseFloat>(dimension - 1));
mat_out->Resize(n_bases, dimension);
for (int32 i = 0; i < n_bases; i++) {
(*mat_out)(i, 0) = 1.0 * scale;
BaseFloat i_fl = static_cast<BaseFloat>(i);
for (int32 j = 1; j < dimension - 1; j++) {
BaseFloat j_fl = static_cast<BaseFloat>(j);
(*mat_out)(i, j) = 2.0 * scale * cos(angle * i_fl * j_fl);
}
(*mat_out)(i, dimension -1)
= scale * cos(angle * i_fl * static_cast<BaseFloat>(dimension-1));
}
}
void SpliceFrames(const MatrixBase<BaseFloat> &input_features,
int32 left_context,
int32 right_context,
Matrix<BaseFloat> *output_features) {
int32 T = input_features.NumRows(), D = input_features.NumCols();
if (T == 0 || D == 0)
KALDI_ERR << "SpliceFrames: empty input";
KALDI_ASSERT(left_context >= 0 && right_context >= 0);
int32 N = 1 + left_context + right_context;
output_features->Resize(T, D*N);
for (int32 t = 0; t < T; t++) {
SubVector<BaseFloat> dst_row(*output_features, t);
for (int32 j = 0; j < N; j++) {
int32 t2 = t + j - left_context;
if (t2 < 0) t2 = 0;
if (t2 >= T) t2 = T-1;
SubVector<BaseFloat> dst(dst_row, j*D, D),
src(input_features, t2);
dst.CopyFromVec(src);
}
}
}
void ReverseFrames(const MatrixBase<BaseFloat> &input_features,
Matrix<BaseFloat> *output_features) {
int32 T = input_features.NumRows(), D = input_features.NumCols();
if (T == 0 || D == 0)
KALDI_ERR << "ReverseFrames: empty input";
output_features->Resize(T, D);
for (int32 t = 0; t < T; t++) {
SubVector<BaseFloat> dst_row(*output_features, t);
SubVector<BaseFloat> src_row(input_features, T-1-t);
dst_row.CopyFromVec(src_row);
}
}
void SlidingWindowCmnOptions::Check() const {
KALDI_ASSERT(cmn_window > 0);
if (center)
KALDI_ASSERT(min_window > 0 && min_window <= cmn_window);
// else ignored so value doesn't matter.
}
// Internal version of SlidingWindowCmn with double-precision arguments.
void SlidingWindowCmnInternal(const SlidingWindowCmnOptions &opts,
const MatrixBase<double> &input,
MatrixBase<double> *output) {
opts.Check();
int32 num_frames = input.NumRows(), dim = input.NumCols(),
last_window_start = -1, last_window_end = -1,
warning_count = 0;
Vector<double> cur_sum(dim), cur_sumsq(dim);
for (int32 t = 0; t < num_frames; t++) {
int32 window_start, window_end; // note: window_end will be one
// past the end of the window we use for normalization.
if (opts.center) {
window_start = t - (opts.cmn_window / 2);
window_end = window_start + opts.cmn_window;
} else {
window_start = t - opts.cmn_window;
window_end = t + 1;
}
if (window_start < 0) { // shift window right if starts <0.
window_end -= window_start;
window_start = 0; // or: window_start -= window_start
}
if (!opts.center) {
if (window_end > t)
window_end = std::max(t + 1, opts.min_window);
}
if (window_end > num_frames) {
window_start -= (window_end - num_frames);
window_end = num_frames;
if (window_start < 0) window_start = 0;
}
if (last_window_start == -1) {
SubMatrix<double> input_part(input,
window_start, window_end - window_start,
0, dim);
cur_sum.AddRowSumMat(1.0, input_part , 0.0);
if (opts.normalize_variance)
cur_sumsq.AddDiagMat2(1.0, input_part, kTrans, 0.0);
} else {
if (window_start > last_window_start) {
KALDI_ASSERT(window_start == last_window_start + 1);
SubVector<double> frame_to_remove(input, last_window_start);
cur_sum.AddVec(-1.0, frame_to_remove);
if (opts.normalize_variance)
cur_sumsq.AddVec2(-1.0, frame_to_remove);
}
if (window_end > last_window_end) {
KALDI_ASSERT(window_end == last_window_end + 1);
SubVector<double> frame_to_add(input, last_window_end);
cur_sum.AddVec(1.0, frame_to_add);
if (opts.normalize_variance)
cur_sumsq.AddVec2(1.0, frame_to_add);
}
}
int32 window_frames = window_end - window_start;
last_window_start = window_start;
last_window_end = window_end;
KALDI_ASSERT(window_frames > 0);
SubVector<double> input_frame(input, t),
output_frame(*output, t);
output_frame.CopyFromVec(input_frame);
output_frame.AddVec(-1.0 / window_frames, cur_sum);
if (opts.normalize_variance) {
if (window_frames == 1) {
output_frame.Set(0.0);
} else {
Vector<double> variance(cur_sumsq);
variance.Scale(1.0 / window_frames);
variance.AddVec2(-1.0 / (window_frames * window_frames), cur_sum);
// now "variance" is the variance of the features in the window,
// around their own mean.
int32 num_floored;
variance.ApplyFloor(1.0e-10, &num_floored);
if (num_floored > 0 && num_frames > 1) {
if (opts.max_warnings == warning_count) {
KALDI_WARN << "Suppressing the remaining variance flooring "
<< "warnings. Run program with --max-warnings=-1 to "
<< "see all warnings.";
}
// If opts.max_warnings is a negative number, we won't restrict the
// number of times that the warning is printed out.
else if (opts.max_warnings < 0
|| opts.max_warnings > warning_count) {
KALDI_WARN << "Flooring when normalizing variance, floored "
<< num_floored << " elements; num-frames was "
<< window_frames;
}
warning_count++;
}
variance.ApplyPow(-0.5); // get inverse standard deviation.
output_frame.MulElements(variance);
}
}
}
}
void SlidingWindowCmn(const SlidingWindowCmnOptions &opts,
const MatrixBase<BaseFloat> &input,
MatrixBase<BaseFloat> *output) {
KALDI_ASSERT(SameDim(input, *output) && input.NumRows() > 0);
Matrix<double> input_dbl(input), output_dbl(input.NumRows(), input.NumCols());
// call double-precision version
SlidingWindowCmnInternal(opts, input_dbl, &output_dbl);
output->CopyFromMat(output_dbl);
}
} // namespace kaldi