tf.contrib.signal.linear_to_mel_weight_matrix

tf.contrib.signal.linear_to_mel_weight_matrix(
    num_mel_bins=20,
    num_spectrogram_bins=129,
    sample_rate=8000,
    lower_edge_hertz=125.0,
    upper_edge_hertz=3800.0,
    dtype=tf.float32,
    name=None
)

Defined in tensorflow/contrib/signal/python/ops/mel_ops.py.

See the guide: Signal Processing (contrib) > Computing log-mel spectrograms

Returns a matrix to warp linear scale spectrograms to the mel scale.

Returns a weight matrix that can be used to re-weight a Tensor containing num_spectrogram_bins linearly sampled frequency information from [0, sample_rate / 2] into num_mel_bins frequency information from [lower_edge_hertz, upper_edge_hertz] on the mel scale.

For example, the returned matrix A can be used to right-multiply a spectrogram S of shape [frames, num_spectrogram_bins] of linear scale spectrum values (e.g. STFT magnitudes) to generate a "mel spectrogram" M of shape [frames, num_mel_bins].

# `S` has shape [frames, num_spectrogram_bins]
# `M` has shape [frames, num_mel_bins]
M = tf.matmul(S, A)

The matrix can be used with tf.tensordot to convert an arbitrary rank Tensor of linear-scale spectral bins into the mel scale.

# S has shape [..., num_spectrogram_bins].
# M has shape [..., num_mel_bins].
M = tf.tensordot(S, A, 1)
# tf.tensordot does not support shape inference for this case yet.
M.set_shape(S.shape[:-1].concatenate(A.shape[-1:]))

Args:

  • num_mel_bins: Python int. How many bands in the resulting mel spectrum.
  • num_spectrogram_bins: Python int. How many bins there are in the source spectrogram data, which is understood to be fft_size // 2 + 1, i.e. the spectrogram only contains the nonredundant FFT bins.
  • sample_rate: Python float. Samples per second of the input signal used to create the spectrogram. We need this to figure out the actual frequencies for each spectrogram bin, which dictates how they are mapped into the mel scale.
  • lower_edge_hertz: Python float. Lower bound on the frequencies to be included in the mel spectrum. This corresponds to the lower edge of the lowest triangular band.
  • upper_edge_hertz: Python float. The desired top edge of the highest frequency band.
  • dtype: The DType of the result matrix. Must be a floating point type.
  • name: An optional name for the operation.

Returns:

A Tensor of shape [num_spectrogram_bins, num_mel_bins].

Raises:

  • ValueError: If num_mel_bins/num_spectrogram_bins/sample_rate are not positive, lower_edge_hertz is negative, frequency edges are incorrectly ordered, or upper_edge_hertz is larger than the Nyquist frequency.