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Diffstat (limited to 'tensorflow/docs_src/api_guides/python/contrib.signal.md')
| -rw-r--r-- | tensorflow/docs_src/api_guides/python/contrib.signal.md | 16 |
1 files changed, 8 insertions, 8 deletions
diff --git a/tensorflow/docs_src/api_guides/python/contrib.signal.md b/tensorflow/docs_src/api_guides/python/contrib.signal.md index 0f7690f80a..66df561084 100644 --- a/tensorflow/docs_src/api_guides/python/contrib.signal.md +++ b/tensorflow/docs_src/api_guides/python/contrib.signal.md @@ -1,7 +1,7 @@ # Signal Processing (contrib) [TOC] -@{tf.contrib.signal} is a module for signal processing primitives. All +`tf.contrib.signal` is a module for signal processing primitives. All operations have GPU support and are differentiable. This module is especially helpful for building TensorFlow models that process or generate audio, though the techniques are useful in many domains. @@ -10,7 +10,7 @@ the techniques are useful in many domains. When dealing with variable length signals (e.g. audio) it is common to "frame" them into multiple fixed length windows. These windows can overlap if the 'step' -of the frame is less than the frame length. @{tf.contrib.signal.frame} does +of the frame is less than the frame length. `tf.contrib.signal.frame` does exactly this. For example: ```python @@ -24,7 +24,7 @@ signals = tf.placeholder(tf.float32, [None, None]) frames = tf.contrib.signal.frame(signals, frame_length=128, frame_step=32) ``` -The `axis` parameter to @{tf.contrib.signal.frame} allows you to frame tensors +The `axis` parameter to `tf.contrib.signal.frame` allows you to frame tensors with inner structure (e.g. a spectrogram): ```python @@ -42,7 +42,7 @@ spectrogram_patches = tf.contrib.signal.frame( ## Reconstructing framed sequences and applying a tapering window -@{tf.contrib.signal.overlap_and_add} can be used to reconstruct a signal from a +`tf.contrib.signal.overlap_and_add` can be used to reconstruct a signal from a framed representation. For example, the following code reconstructs the signal produced in the preceding example: @@ -58,7 +58,7 @@ the resulting reconstruction will have a greater magnitude than the original window function satisfies the Constant Overlap-Add (COLA) property for the given frame step, then it will recover the original `signals`. -@{tf.contrib.signal.hamming_window} and @{tf.contrib.signal.hann_window} both +`tf.contrib.signal.hamming_window` and `tf.contrib.signal.hann_window` both satisfy the COLA property for a 75% overlap. ```python @@ -74,7 +74,7 @@ reconstructed_signals = tf.contrib.signal.overlap_and_add( A spectrogram is a time-frequency decomposition of a signal that indicates its frequency content over time. The most common approach to computing spectrograms is to take the magnitude of the [Short-time Fourier Transform][stft] (STFT), -which @{tf.contrib.signal.stft} can compute as follows: +which `tf.contrib.signal.stft` can compute as follows: ```python # A batch of float32 time-domain signals in the range [-1, 1] with shape @@ -121,7 +121,7 @@ When working with spectral representations of audio, the [mel scale][mel] is a common reweighting of the frequency dimension, which results in a lower-dimensional and more perceptually-relevant representation of the audio. -@{tf.contrib.signal.linear_to_mel_weight_matrix} produces a matrix you can use +`tf.contrib.signal.linear_to_mel_weight_matrix` produces a matrix you can use to convert a spectrogram to the mel scale. ```python @@ -156,7 +156,7 @@ log_mel_spectrograms = tf.log(mel_spectrograms + log_offset) ## Computing Mel-Frequency Cepstral Coefficients (MFCCs) -Call @{tf.contrib.signal.mfccs_from_log_mel_spectrograms} to compute +Call `tf.contrib.signal.mfccs_from_log_mel_spectrograms` to compute [MFCCs][mfcc] from log-magnitude, mel-scale spectrograms (as computed in the preceding example): |