tf.signal.linear_to_mel_weight_matrix

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.

This function follows the Hidden Markov Model Toolkit (HTK) convention, defining the mel scale in terms of a frequency in hertz according to the following formula:

$$ extrm{mel}(f) = 2595 * extrm{log}_{10}(1 + rac{f}{700})$$

In the returned matrix, all the triangles (filterbanks) have a peak value of 1.0.

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)

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 An integer or float Tensor. Samples per second of the input signal used to create the spectrogram. Used to figure out the frequencies corresponding to 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