tf.signal.linear_to_mel_weight_matrix

TensorFlow 1 version View source on GitHub

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

tf.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.dtypes.float32,
    name=None
)

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:]))

num_mel_bins Python int. How many bands in the resulting mel spectrum.
num_spectrogram_bins An integer Tensor. 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.

A Tensor of shape [num_spectrogram_bins, num_mel_bins].

ValueError If num_mel_bins/num_spectrogram_bins/sample_rate are not positive, lower_edge_hertz is negative, frequency edges are incorrectly ordered, upper_edge_hertz is larger than the Nyquist frequency, or sample_rate is neither a Python float nor a constant Tensor.