PiperOrigin-RevId: 170752644

4 files changed, 0 insertions, 271 deletions

diff --git a/tensorflow/contrib/signal/BUILD b/tensorflow/contrib/signal/BUILD
index 6025ec5b57..8c11cf0d64 100644
--- a/tensorflow/contrib/signal/BUILD
+++ b/tensorflow/contrib/signal/BUILD
@@ -35,20 +35,6 @@ cuda_py_tests(
 )
 
 cuda_py_tests(
-    name = "mfcc_ops_test",
-    srcs = ["python/kernel_tests/mfcc_ops_test.py"],
-    additional_deps = [
-        ":signal_py",
-        "//third_party/py/numpy",
-        "//tensorflow/python:client_testlib",
-        "//tensorflow/python:framework",
-        "//tensorflow/python:framework_for_generated_wrappers",
-        "//tensorflow/python:framework_test_lib",
-        "//tensorflow/python:spectral_ops_test_util",
-    ],
-)
-
-cuda_py_tests(
     name = "reconstruction_ops_test",
     srcs = ["python/kernel_tests/reconstruction_ops_test.py"],
     additional_deps = [
diff --git a/tensorflow/contrib/signal/__init__.py b/tensorflow/contrib/signal/__init__.py
index 0f2592b0b0..25123b097e 100644
--- a/tensorflow/contrib/signal/__init__.py
+++ b/tensorflow/contrib/signal/__init__.py
@@ -20,7 +20,6 @@ See the @{$python/contrib.signal} guide.
 @@hamming_window
 @@hann_window
 @@inverse_stft
-@@mfccs_from_log_mel_spectrograms
 @@linear_to_mel_weight_matrix
 @@overlap_and_add
 @@stft
@@ -28,7 +27,6 @@ See the @{$python/contrib.signal} guide.
 [hamming]: https://en.wikipedia.org/wiki/Window_function#Hamming_window
 [hann]: https://en.wikipedia.org/wiki/Window_function#Hann_window
 [mel]: https://en.wikipedia.org/wiki/Mel_scale
-[mfcc]: https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
 [stft]: https://en.wikipedia.org/wiki/Short-time_Fourier_transform
 """
 
@@ -37,7 +35,6 @@ from __future__ import division
 from __future__ import print_function
 
 from tensorflow.contrib.signal.python.ops.mel_ops import linear_to_mel_weight_matrix
-from tensorflow.contrib.signal.python.ops.mfcc_ops import mfccs_from_log_mel_spectrograms
 from tensorflow.contrib.signal.python.ops.reconstruction_ops import overlap_and_add
 from tensorflow.contrib.signal.python.ops.shape_ops import frame
 # `frame` used to be named `frames`, which is a noun and not a verb.
diff --git a/tensorflow/contrib/signal/python/kernel_tests/mfcc_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/mfcc_ops_test.py
deleted file mode 100644
index b3a8d40c13..0000000000
--- a/tensorflow/contrib/signal/python/kernel_tests/mfcc_ops_test.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# 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.
-# ==============================================================================
-"""Tests for mfcc_ops."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import importlib
-
-import numpy as np
-
-
-from tensorflow.contrib.signal.python.ops import mfcc_ops
-from tensorflow.python.framework import dtypes
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import random_ops
-from tensorflow.python.ops import spectral_ops_test_util
-from tensorflow.python.platform import test
-from tensorflow.python.platform import tf_logging
-
-
-# TODO(rjryan): Add scipy.fftpack to the TensorFlow build.
-def try_import(name):  # pylint: disable=invalid-name
-  module = None
-  try:
-    module = importlib.import_module(name)
-  except ImportError as e:
-    tf_logging.warning("Could not import %s: %s" % (name, str(e)))
-  return module
-
-
-fftpack = try_import("scipy.fftpack")
-
-
-class DCTTest(test.TestCase):
-
-  def _np_dct2(self, signals, norm=None):
-    """Computes the DCT-II manually with NumPy."""
-    # X_k = sum_{n=0}^{N-1} x_n * cos(\frac{pi}{N} * (n + 0.5) * k)  k=0,...,N-1
-    dct_size = signals.shape[-1]
-    dct = np.zeros_like(signals)
-    for k in range(dct_size):
-      phi = np.cos(np.pi * (np.arange(dct_size) + 0.5) * k / dct_size)
-      dct[..., k] = np.sum(signals * phi, axis=-1)
-    # SciPy's `dct` has a scaling factor of 2.0 which we follow.
-    # https://github.com/scipy/scipy/blob/v0.15.1/scipy/fftpack/src/dct.c.src
-    if norm == "ortho":
-      # The orthogonal scaling includes a factor of 0.5 which we combine with
-      # the overall scaling of 2.0 to cancel.
-      dct[..., 0] *= np.sqrt(1.0 / dct_size)
-      dct[..., 1:] *= np.sqrt(2.0 / dct_size)
-    else:
-      dct *= 2.0
-    return dct
-
-  def test_compare_to_numpy(self):
-    """Compare dct against a manual DCT-II implementation."""
-    with spectral_ops_test_util.fft_kernel_label_map():
-      with self.test_session(use_gpu=True):
-        for size in range(1, 23):
-          signals = np.random.rand(size).astype(np.float32)
-          actual_dct = mfcc_ops._dct2_1d(signals).eval()
-          expected_dct = self._np_dct2(signals)
-          self.assertAllClose(expected_dct, actual_dct, atol=5e-4, rtol=5e-4)
-
-  def test_compare_to_fftpack(self):
-    """Compare dct against scipy.fftpack.dct."""
-    if not fftpack:
-      return
-    with spectral_ops_test_util.fft_kernel_label_map():
-      with self.test_session(use_gpu=True):
-        for size in range(1, 23):
-          signal = np.random.rand(size).astype(np.float32)
-          actual_dct = mfcc_ops._dct2_1d(signal).eval()
-          expected_dct = fftpack.dct(signal, type=2)
-          self.assertAllClose(expected_dct, actual_dct, atol=5e-4, rtol=5e-4)
-
-
-# TODO(rjryan): We have no open source tests for MFCCs at the moment. Internally
-# at Google, this code is tested against a reference implementation that follows
-# HTK conventions.
-class MFCCTest(test.TestCase):
-
-  def test_error(self):
-    # num_mel_bins must be positive.
-    with self.assertRaises(ValueError):
-      signal = array_ops.zeros((2, 3, 0))
-      mfcc_ops.mfccs_from_log_mel_spectrograms(signal)
-
-    # signal must be float32
-    with self.assertRaises(ValueError):
-      signal = array_ops.zeros((2, 3, 5), dtype=dtypes.float64)
-      mfcc_ops.mfccs_from_log_mel_spectrograms(signal)
-
-  def test_basic(self):
-    """A basic test that the op runs on random input."""
-    with spectral_ops_test_util.fft_kernel_label_map():
-      with self.test_session(use_gpu=True):
-        signal = random_ops.random_normal((2, 3, 5))
-        mfcc_ops.mfccs_from_log_mel_spectrograms(signal).eval()
-
-
-if __name__ == "__main__":
-  test.main()
diff --git a/tensorflow/contrib/signal/python/ops/mfcc_ops.py b/tensorflow/contrib/signal/python/ops/mfcc_ops.py
deleted file mode 100644
index 35b6d3ad45..0000000000
--- a/tensorflow/contrib/signal/python/ops/mfcc_ops.py
+++ /dev/null
@@ -1,137 +0,0 @@
-# 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.
-# ==============================================================================
-"""Mel-Frequency Cepstral Coefficients (MFCCs) ops."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import math
-
-from tensorflow.python.framework import dtypes
-from tensorflow.python.framework import ops
-from tensorflow.python.ops import array_ops
-from tensorflow.python.ops import math_ops
-from tensorflow.python.ops import spectral_ops
-
-
-# TODO(rjryan): Remove once tf.spectral.dct exists.
-def _dct2_1d(signals, name=None):
-  """Computes the type II 1D Discrete Cosine Transform (DCT) of `signals`.
-
-  Args:
-    signals: A `[..., samples]` `float32` `Tensor` containing the signals to
-      take the DCT of.
-    name: An optional name for the operation.
-
-  Returns:
-    A `[..., samples]` `float32` `Tensor` containing the DCT of `signals`.
-
-  """
-  with ops.name_scope(name, 'dct', [signals]):
-    # We use the FFT to compute the DCT and TensorFlow only supports float32 for
-    # FFTs at the moment.
-    signals = ops.convert_to_tensor(signals, dtype=dtypes.float32)
-
-    axis_dim = signals.shape[-1].value or array_ops.shape(signals)[-1]
-    axis_dim_float = math_ops.to_float(axis_dim)
-    scale = 2.0 * math_ops.exp(math_ops.complex(
-        0.0, -math.pi * math_ops.range(axis_dim_float) /
-        (2.0 * axis_dim_float)))
-
-    rfft = spectral_ops.rfft(signals, fft_length=[2 * axis_dim])[..., :axis_dim]
-    dct2 = math_ops.real(rfft * scale)
-    return dct2
-
-
-def mfccs_from_log_mel_spectrograms(log_mel_spectrograms, name=None):
-  """Computes [MFCCs][mfcc] of `log_mel_spectrograms`.
-
-  Implemented with GPU-compatible ops and supports gradients.
-
-  [Mel-Frequency Cepstral Coefficient (MFCC)][mfcc] calculation consists of
-  taking the DCT-II of a log-magnitude mel-scale spectrogram. [HTK][htk]'s MFCCs
-  use a particular scaling of the DCT-II which is almost orthogonal
-  normalization. We follow this convention.
-
-  All `num_mel_bins` MFCCs are returned and it is up to the caller to select
-  a subset of the MFCCs based on their application. For example, it is typical
-  to only use the first few for speech recognition, as this results in
-  an approximately pitch-invariant representation of the signal.
-
-  For example:
-
-  ```python
-  sample_rate = 16000.0
-  # A Tensor of [batch_size, num_samples] mono PCM samples in the range [-1, 1].
-  pcm = tf.placeholder(tf.float32, [None, None])
-
-  # A 1024-point STFT with frames of 64 ms and 75% overlap.
-  stfts = tf.contrib.signal.stft(pcm, frame_length=1024, frame_step=256,
-                                 fft_length=1024)
-  spectrograms = tf.abs(stft)
-
-  # Warp the linear scale spectrograms into the mel-scale.
-  num_spectrogram_bins = stfts.shape[-1].value
-  lower_edge_hertz, upper_edge_hertz, num_mel_bins = 80.0, 7600.0, 80
-  linear_to_mel_weight_matrix = tf.contrib.signal.linear_to_mel_weight_matrix(
-    num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz,
-    upper_edge_hertz)
-  mel_spectrograms = tf.tensordot(
-    spectrograms, linear_to_mel_weight_matrix, 1)
-  mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
-    linear_to_mel_weight_matrix.shape[-1:]))
-
-  # Compute a stabilized log to get log-magnitude mel-scale spectrograms.
-  log_mel_spectrograms = tf.log(mel_spectrograms + 1e-6)
-
-  # Compute MFCCs from log_mel_spectrograms and take the first 13.
-  mfccs = tf.contrib.signal.mfccs_from_log_mel_spectrograms(
-    log_mel_spectrograms)[..., :13]
-  ```
-
-  Args:
-    log_mel_spectrograms: A `[..., num_mel_bins]` `float32` `Tensor` of
-      log-magnitude mel-scale spectrograms.
-    name: An optional name for the operation.
-  Returns:
-    A `[..., num_mel_bins]` `float32` `Tensor` of the MFCCs of
-    `log_mel_spectrograms`.
-
-  Raises:
-    ValueError: If `num_mel_bins` is not positive.
-
-  [mfcc]: https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
-  [htk]: https://en.wikipedia.org/wiki/HTK_(software)
-  """
-  with ops.name_scope(name, 'mfccs_from_log_mel_spectrograms',
-                      [log_mel_spectrograms]):
-    # Compute the DCT-II of the resulting log-magnitude mel-scale spectrogram.
-    # The DCT used in HTK scales every basis vector by sqrt(2/N), which is the
-    # scaling required for an "orthogonal" DCT-II *except* in the 0th bin, where
-    # the true orthogonal DCT (as implemented by scipy) scales by sqrt(1/N). For
-    # this reason, we don't apply orthogonal normalization and scale the DCT by
-    # `0.5 * sqrt(2/N)` manually.
-    log_mel_spectrograms = ops.convert_to_tensor(log_mel_spectrograms,
-                                                 dtype=dtypes.float32)
-    if (log_mel_spectrograms.shape.ndims and
-        log_mel_spectrograms.shape[-1].value is not None):
-      num_mel_bins = log_mel_spectrograms.shape[-1].value
-      if num_mel_bins == 0:
-        raise ValueError('num_mel_bins must be positive. Got: %s' %
-                         log_mel_spectrograms)
-    else:
-      num_mel_bins = array_ops.shape(log_mel_spectrograms)[-1]
-    return _dct2_1d(log_mel_spectrograms) * math_ops.rsqrt(num_mel_bins * 2.0)