athena.transform.feats.mfcc

This model extracts MFCC features per frame.

Module Contents

Classes

Mfcc	Compute mfcc features of every frame in speech, return a float tensor

class athena.transform.feats.mfcc.Mfcc(config: dict)

Bases: athena.transform.feats.base_frontend.BaseFrontend

Compute mfcc features of every frame in speech, return a float tensor with size (num_channels, num_frames, num_frequencies).

classmethod params(cls, config=None)

Set params. :param config: contains fourteen optional parameters.

window_length: Window length in seconds. (float, default = 0.025) frame_length: Hop length in seconds. (float, default = 0.010) snip_edges: If 1, the last frame (shorter than window_length) will

be cutoff. If 2, 1 // 2 frame_length data will be padded to data. (int, default = 1)

raw_energy: If 1, compute frame energy before preemphasis and

windowing. If 2, compute frame energy after preemphasis and windowing. (int, default = 1)

preEph_coeff: Coefficient for use in frame-signal preemphasis.

(float, default = 0.97)

window_type: Type of window (“hamm”|”hann”|”povey”|”rect”|”blac”|”tria”).

(string, default = “povey”)

remove_dc_offset: Subtract mean from waveform on each frame

(bool, default = true)

is_fbank: If true, compute power spetrum without frame energy. If

false, using the frame energy instead of the square of the constant component of the signal. (bool, default = true)

output_type: If 1, return power spectrum. If 2, return log-power

spectrum. (int, default = 1)

upper_frequency_limit: High cutoff frequency for mel bins (if < 0, offset from

Nyquist) (float, default = 0)

lower_frequency_limit: Low cutoff frequency for mel bins (float, default = 20) filterbank_channel_count: Number of triangular mel-frequency bins.

(float, default = 23)

coefficient_count: Number of cepstra in MFCC computation.

(int, default = 13)

cepstral_lifter: Constant that controls scaling of MFCCs.

(float, default = 22)

use_energy:Use energy (not C0) in MFCC computation.

(bool, default = True)

Returns:An object of class HParams, which is a set of hyperparameters as name-value pairs.

call(self, audio_data, sample_rate)

Caculate mfcc features of audio data. :param audio_data: the audio signal from which to compute spectrum.

Should be an (1, N) tensor.

Parameters:sample_rate – the sample rate of the signal we working with. Returns:A float tensor of size (num_channels, num_frames, num_frequencies) containing mfcc features of every frame in speech.

dim(self)

dim