1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
|
# 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.
# ==============================================================================
"""Signal reconstruction via overlapped addition of frames."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.signal.python.ops import shape_ops
from tensorflow.contrib.signal.python.ops import util_ops
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
def _shuffle_to_front(input_tensor, k):
"""Shuffles the last `k` indices of `input_tensor` to the front.
Transposes `input_tensor` to have the last `k` indices at the front. The input
may have arbitrary rank and unknown shape.
Args:
input_tensor: A `Tensor` of arbitrary rank and unknown shape.
k: A scalar `Tensor` specifying how many indices to shuffle.
Returns:
A transposed version of `input_tensor` with `k` indices shuffled to the
front.
Raises:
ValueError: If `input_tensor` is not at least rank `k` or `k` is not scalar.
"""
k = ops.convert_to_tensor(k, name="k")
k.shape.with_rank(0)
k_static = tensor_util.constant_value(k)
if k_static is not None:
input_tensor.shape.with_rank_at_least(k_static)
rank = array_ops.rank(input_tensor)
outer_indices, inner_indices = array_ops.split(math_ops.range(rank),
[rank - k, k])
permutation = array_ops.concat([inner_indices, outer_indices], 0)
return array_ops.transpose(input_tensor, perm=permutation)
def overlap_and_add(signal, frame_step, name=None):
"""Reconstructs a signal from a framed representation.
Adds potentially overlapping frames of a signal with shape
`[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.
The resulting tensor has shape `[..., output_size]` where
output_size = (frames - 1) * frame_step + frame_length
Args:
signal: A [..., frames, frame_length] `Tensor`. All dimensions may be
unknown, and rank must be at least 2.
frame_step: An integer or scalar `Tensor` denoting overlap offsets. Must be
less than or equal to `frame_length`.
name: An optional name for the operation.
Returns:
A `Tensor` with shape `[..., output_size]` containing the overlap-added
frames of `signal`'s inner-most two dimensions.
Raises:
ValueError: If `signal`'s rank is less than 2, `frame_step` is not a scalar
integer or `frame_step` is greater than `frame_length`.
"""
with ops.name_scope(name, "overlap_and_add", [signal, frame_step]):
signal = ops.convert_to_tensor(signal, name="signal")
signal.shape.with_rank_at_least(2)
frame_step = ops.convert_to_tensor(frame_step, name="frame_step")
frame_step.shape.assert_has_rank(0)
if not frame_step.dtype.is_integer:
raise ValueError("frame_step must be an integer. Got %s" %
frame_step.dtype)
# If frame_length and frame_step are known at graph construction time, check
# frame_step is less than or equal to frame_length.
frame_step_static = tensor_util.constant_value(frame_step)
if (frame_step_static is not None and signal.shape.ndims is not None and
signal.shape[-1].value is not None and
frame_step_static > signal.shape[-1].value):
raise ValueError(
"frame_step (%d) must be less than or equal to frame_length (%d)" % (
frame_step_static, signal.shape[-1].value))
signal_shape = array_ops.shape(signal)
# All dimensions that are not part of the overlap-and-add. Can be empty for
# rank 2 inputs.
outer_dimensions = signal_shape[:-2]
signal_rank = array_ops.rank(signal)
frames = signal_shape[-2]
frame_length = signal_shape[-1]
subframe_length = util_ops.gcd(frame_length, frame_step)
subframe_step = frame_step // subframe_length
subframes_per_frame = frame_length // subframe_length
output_size = frame_step * (frames - 1) + frame_length
output_subframes = output_size // subframe_length
# To avoid overlap-adding sample-by-sample, we overlap-add at the "subframe"
# level, where a subframe is gcd(frame_length, frame_step). Reshape signal
# from [..., frames, frame_length] into [..., subframes, subframe_length].
subframe_shape = array_ops.concat(
[outer_dimensions, [-1, subframe_length]], 0)
subframe_signal = array_ops.reshape(signal, subframe_shape)
# Now we shuffle the last [subframes, subframe_length] dimensions to the
# front.
# TODO(rjryan): Add an axis argument to unsorted_segment_sum so we can
# avoid this pair of transposes.
subframe_signal = _shuffle_to_front(subframe_signal, 2)
# Use unsorted_segment_sum to add overlapping subframes together.
segment_ids = array_ops.reshape(shape_ops.frame(
math_ops.range(output_subframes), subframes_per_frame, subframe_step,
pad_end=False), [-1])
result = math_ops.unsorted_segment_sum(subframe_signal, segment_ids,
num_segments=output_subframes)
# result is a [subframes, subframe_length, ...outer_dimensions] tensor. We
# return a [...outer_dimensions, output_size] tensor with a transpose and
# reshape.
result_shape = array_ops.concat([outer_dimensions, [output_size]], 0)
return array_ops.reshape(_shuffle_to_front(result, signal_rank - 2),
result_shape)
|
