tf.signal.mfccs_from_log_mel_spectrograms

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Computes MFCCs of log_mel_spectrograms.

View aliases

Main aliases

`tf.contrib.signal.mfccs_from_log_mel_spectrograms`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.signal.mfccs_from_log_mel_spectrograms, `tf.compat.v2.signal.mfccs_from_log_mel_spectrograms`

tf.signal.mfccs_from_log_mel_spectrograms(
    log_mel_spectrograms, name=None
)

Implemented with GPU-compatible ops and supports gradients.

Mel-Frequency Cepstral Coefficient (MFCC) calculation consists of taking the DCT-II of a log-magnitude mel-scale spectrogram. 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:

sample_rate = 16000.0
# A Tensor of [batch_size, num_samples] mono PCM samples in the range [-1, 1].
pcm = tf.compat.v1.placeholder(tf.float32, [None, None])

# A 1024-point STFT with frames of 64 ms and 75% overlap.
stfts = tf.signal.stft(pcm, frame_length=1024, frame_step=256,
                       fft_length=1024)
spectrograms = tf.abs(stfts)

# 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.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.math.log(mel_spectrograms + 1e-6)

# Compute MFCCs from log_mel_spectrograms and take the first 13.
mfccs = tf.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.