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Commit a3fc0af9 authored by MARMORET Axel's avatar MARMORET Axel
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Modifying 'features' into 'signal_to_spectrogram'

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as_seg/example.py
+ 3
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View file @ a3fc0af9
......@@ -18,7 +18,7 @@ import librosa
import mirdata
# Module encapsulating the computation of features from the signal
import as_seg.model.features as features
import as_seg.model.signal_to_spectrogram as signal_to_spectrogram
# General module for manipulating data: conversion between time, bars, frame indexes, loading of data, ...
import as_seg.data_manipulation as dm
......@@ -36,7 +36,7 @@ import as_seg.CBM_algorithm as cbm
from as_seg.model.current_plot import *
# %% Loading annotations and defining the audio path
path_to_beatles_dataset = 'C:/Users/amarmore/this_folder/Beatles dataset/' # To change
path_to_beatles_dataset = '/home/a23marmo/datasets/beatles' # To change
beatles = mirdata.initialize('beatles', path_to_beatles_dataset)
beatles.download()
......@@ -56,7 +56,7 @@ hop_length = 32 # Oversampling the spectrogram, to select frames which will be e
hop_length_seconds = hop_length/sampling_rate # As bars are in seconds, we convert this hop length in seconds.
subdivision_bars = 96 # The number of time samples to consider in each bar.
log_mel = features.get_spectrogram(the_signal, sampling_rate, "log_mel_grill", hop_length = hop_length) # Log_mel spectrogram
log_mel = signal_to_spectrogram.get_spectrogram(the_signal, sampling_rate, "log_mel", hop_length = hop_length) # Log_mel spectrogram
# %% Cosine autosimilarity
barwise_TF_cosine = bi.barwise_TF_matrix(log_mel, bars, hop_length_seconds, subdivision_bars)
......
as_seg/model/features.py deleted 100755 → 0
+ 0
− 345
View file @ 50ef4102
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 16:54:59 2020
@author: amarmore
Computing spectrogram in different feature description.
Note that Mel (and variants of Mel) spectrograms are denoted "mel_grill",
as they follow the particular definition of [1].
[1] Grill, T., & Schlüter, J. (2015, October).
Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations.
In ISMIR (pp. 531-537).
"""
import numpy as np
import librosa.core
import librosa.feature
import librosa.effects
from math import inf
import as_seg.model.errors as err
import IPython.display as ipd
def get_spectrogram(signal, sr, feature, hop_length, n_fft = 2048, fmin = 98, n_mfcc = 20):
"""
Returns a spectrogram, from the signal of a song.
Different types of spectrogram can be computed, which are specified by the argument "feature".
All these spectrograms are computed with the toolbox librosa [1].
Parameters
----------
signal : numpy array
Signal of the song.
sr : float
Sampling rate of the signal, (typically 44100Hz).
feature : String
The types of spectrograms to compute.
- stft : computes the Short-Time Fourier Transform of the signal.
Returns the Power spectrogram.
- pcp : computes a chromagram.
NB: this chromagram has been specificly fitted as a team,
and the arguments are non standard but rather technical choices.
- pcp_stft : computes a chromagram from the stft of the song.
- cqt : computes a Constant-Q transform of the song.
- log_cqt : computes the logarithm of the Constant-Q transform of the song.
- tonnetz : computes the tonnetz representation of the song.
- pcp_tonnetz : computes the tonnetz representation of the song, starting from the chromas.
It allows us to better control paramaters over the computation of tonnetz,
and can reduce computation when chromas are already computed (for scripts loading already computed spectrograms).
- mfcc : computes the Mel-Frequency Cepstral Coefficients of the song.
- mel_grill : computes the mel-spectrogram of the song, as dimensioned by [2].
- log_mel_grill : computes the logarithm of the previously defined mel-spectrogram.
- pos_log_mel_grill : computes the log(mel + 1) of the previously defined mel-spectrogram.
hop_length : integer
The desired hop_length, which is the step between two frames (ie the time "discretization" step)
It is expressed in terms of number of samples, which are defined by the sampling rate.
n_fft : integer, optional
Number of frames by stft feature.
The default is 2048.
fmin : integer, optional
The minimal frequence to consider, used for denoising.
The default is 98.
n_mfcc : integer, optional
Number of mfcc features.
The default is 20 (as in librosa).
Raises
------
InvalidArgumentValueException
If the "feature" argument is not presented above.
Returns
-------
numpy array
Spectrogram of the signal.
References
----------
[1] McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., & Nieto, O. (2015, July).
librosa: Audio and music signal analysis in python.
In Proceedings of the 14th python in science conference (Vol. 8).
[2] Grill, T., & Schlüter, J. (2015, October).
Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations.
In ISMIR (pp. 531-537).
"""
if feature.lower() == "stft":
if len(signal.shape) == 1:
stft = librosa.core.stft(y=np.asfortranarray(signal), n_fft=n_fft, hop_length = hop_length)
power_spectrogram = np.abs(stft) ** 2
return power_spectrogram
power_spectrogram = np.abs(librosa.core.stft(y=np.asfortranarray(signal[:,0]), n_fft=n_fft, hop_length = hop_length))**2
for i in range(1,signal.shape[1]):
power_spectrogram += np.abs(librosa.core.stft(y=np.asfortranarray(signal[:,i]), n_fft=n_fft, hop_length = hop_length))**2
return power_spectrogram
elif feature.lower() == "pcp_stft":
if len(signal.shape) == 1:
audio_harmonic, _ = librosa.effects.hpss(y=np.asfortranarray(signal))
chroma_stft = librosa.feature.chroma_stft(y=audio_harmonic, sr=sr, n_fft = n_fft, hop_length=hop_length)
return chroma_stft
audio_harmonic, _ = librosa.effects.hpss(y=np.asfortranarray(signal[:,0]))
chroma_stft = librosa.feature.chroma_stft(y=audio_harmonic, sr=sr, n_fft = n_fft, hop_length=hop_length)
for i in range(1,signal.shape[1]):
audio_harmonic, _ = librosa.effects.hpss(y=np.asfortranarray(signal[:,i]))
chroma_stft += librosa.feature.chroma_stft(y=audio_harmonic, sr=sr, n_fft = n_fft, hop_length=hop_length)
return chroma_stft
elif feature == "pcp":
norm=inf # Columns normalization
win_len_smooth=82 # Size of the smoothign window
n_octaves=6
bins_per_chroma = 3
bins_per_octave=bins_per_chroma * 12
if len(signal.shape) == 1:
return librosa.feature.chroma_cens(y=np.asfortranarray(signal),sr=sr,hop_length=hop_length,
fmin=fmin, n_chroma=12, n_octaves=n_octaves, bins_per_octave=bins_per_octave,
norm=norm, win_len_smooth=win_len_smooth)
pcp = librosa.feature.chroma_cens(y=np.asfortranarray(signal[:,0]),sr=sr,hop_length=hop_length,
fmin=fmin, n_chroma=12, n_octaves=n_octaves, bins_per_octave=bins_per_octave,
norm=norm, win_len_smooth=win_len_smooth)
for i in range(1,signal.shape[1]):
pcp += librosa.feature.chroma_cens(y=np.asfortranarray(signal[:,i]),sr=sr,hop_length=hop_length,
fmin=fmin, n_chroma=12, n_octaves=n_octaves, bins_per_octave=bins_per_octave,
norm=norm, win_len_smooth=win_len_smooth)
return pcp
elif feature.lower() == "cqt":
if len(signal.shape) == 1:
constant_q_transf = librosa.core.cqt(y=np.asfortranarray(signal), sr = sr, hop_length = hop_length)
power_cqt = np.abs(constant_q_transf) ** 2
return power_cqt
power_cqt = np.abs(librosa.core.cqt(y=np.asfortranarray(signal[:,0]), sr = sr, hop_length = hop_length)) ** 2
for i in range(1,signal.shape[1]):
power_cqt += np.abs(librosa.core.cqt(y=np.asfortranarray(signal[:,i]), sr = sr, hop_length = hop_length)) ** 2
return power_cqt
elif feature.lower() == "log_cqt":
if len(signal.shape) == 1:
constant_q_transf = librosa.core.cqt(y=np.asfortranarray(signal), sr = sr, hop_length = hop_length)
power_cqt = np.abs(constant_q_transf) ** 2
log_cqt = ((1.0/80.0) * librosa.core.amplitude_to_db(y=np.abs(np.array(power_cqt)), ref=np.max)) + 1.0
return log_cqt
power_cqt = np.abs(librosa.core.cqt(y=np.asfortranarray(signal[:,0]), sr = sr, hop_length = hop_length)) ** 2
for i in range(1,signal.shape[1]):
power_cqt += np.abs(librosa.core.cqt(y=np.asfortranarray(signal[:,i]), sr = sr, hop_length = hop_length)) ** 2
log_cqt = ((1.0/80.0) * librosa.core.amplitude_to_db(y=np.abs(np.array(power_cqt)), ref=np.max)) + 1.0
return log_cqt
elif feature.lower() == "tonnetz":
if len(signal.shape) == 1:
return librosa.feature.tonnetz(y=np.asfortranarray(signal), sr = sr)
tonnetz = librosa.feature.tonnetz(y=np.asfortranarray(signal[:,0]), sr = sr)
for i in range(1,signal.shape[1]):
tonnetz += librosa.feature.tonnetz(y=np.asfortranarray(signal[:,i]), sr = sr)
return tonnetz
elif feature.lower() == "pcp_tonnetz":
return librosa.feature.tonnetz(y=None, sr = None, chroma = get_spectrogram(signal, sr, "pcp", hop_length, fmin = fmin))
# elif feature.lower() == "hcqt":
# return my_compute_hcqt(np.asfortranarray(signal[:,0]), sr)
elif feature.lower() == "mfcc":
if len(signal.shape) == 1:
return librosa.feature.mfcc(y=np.asfortranarray(signal), sr = sr, hop_length = hop_length, n_mfcc=n_mfcc)
mfcc = librosa.feature.mfcc(y=np.asfortranarray(signal[:,0]), sr = sr, hop_length = hop_length, n_mfcc=n_mfcc)
for i in range(1,signal.shape[1]):
mfcc += librosa.feature.mfcc(y=np.asfortranarray(signal[:,i]), sr = sr, hop_length = hop_length, n_mfcc=n_mfcc)
return mfcc
# For Mel spectrograms, we use the same parameters as the ones of [2].
# [2] Grill, Thomas, and Jan Schlüter. "Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations." ISMIR. 2015.
elif feature.lower() == "mel_grill":
if len(signal.shape) == 1:
return np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
mel = np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,0]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
for i in range(1,signal.shape[1]):
mel += np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,i]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
return mel
elif feature == "log_mel_grill":
if len(signal.shape) == 1:
return librosa.power_to_db(np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000)))
mel = np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,0]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
for i in range(1,signal.shape[1]):
mel += np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,i]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
return librosa.power_to_db(mel)
elif feature == "nn_log_mel_grill":
if len(signal.shape) == 1:
mel = np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
return librosa.power_to_db(mel + np.ones(mel.shape))
mel = np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,0]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
for i in range(1,signal.shape[1]):
mel += np.abs(librosa.feature.melspectrogram(y=np.asfortranarray(signal[:,i]), sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000))
return librosa.power_to_db(mel + np.ones(mel.shape))
elif feature == "padded_log_mel_grill":
log_mel = get_spectrogram(signal, sr, "log_mel_grill", hop_length)
return log_mel - np.amin(log_mel) * np.ones(log_mel.shape)
elif feature == "mel" or feature == "log_mel" or feature == "nn_log_mel":
raise err.InvalidArgumentValueException("Invalid feature parameter, aren't you looking for mel_grill/log_mel_grill (the only available Mel Spectrograms)?")
else:
raise err.InvalidArgumentValueException(f"Unknown signal representation: {feature}.")
def get_log_mel_from_mel(mel_spectrogram, feature):
"""
Computes a variant of a Mel spectrogram (typically Log Mel).
Parameters
----------
mel_spectrogram : numpy array
Mel spectrogram of the signal.
feature : string
Desired feature name (must be a variant of a Mel spectrogram).
Raises
------
err.InvalidArgumentValueException
Raised in case of unknown feature name.
Returns
-------
numpy array
Variant of the Mel spectrogram of the signal.
"""
if feature == "log_mel_grill":
return librosa.power_to_db(np.abs(mel_spectrogram))
elif feature == "nn_log_mel_grill":
return librosa.power_to_db(mel_spectrogram + np.ones(mel_spectrogram.shape))
elif feature == "padded_log_mel_grill":
log_mel = get_log_mel_from_mel(mel_spectrogram, "log_mel_grill")
return log_mel - np.amin(log_mel) * np.ones(log_mel.shape)
elif feature == "minmax_log_mel_grill":
padded_log_mel = get_log_mel_from_mel(mel_spectrogram, "padded_log_mel_grill")
return np.divide(padded_log_mel, np.amax(padded_log_mel))
elif feature == "mel" or feature == "log_mel":
raise err.InvalidArgumentValueException("Invalid mel parameter, are't you looking for mel_grill?")
else:
raise err.InvalidArgumentValueException("Unknown feature representation.")
def get_audio_from_spectrogram(spectrogram, feature, hop_length, sr):
"""
Computes an audio signal for a COMPLEX-valued spectrogram.
Parameters
----------
spectrogram : numpy array
Complex-valued spectrogram.
feature : string
Name of the particular feature used for representing the signal in a spectrogram.
hop_length : int
Hop length of the spectrogram
(Or similar value for the reconstruction to make sense).
sr : inteer
Sampling rate of the signal, when processed into a spectrogram
(Or similar value for the reconstruction to make sense).
Raises
------
InvalidArgumentValueException
In case of an unknown feature representation.
Returns
-------
ipd.Audio
Audio signal of the spectrogram.
"""
if feature == "stft":
audio = librosa.griffinlim(spectrogram, hop_length = hop_length)
return ipd.Audio(audio, rate=sr)
elif feature == "mel_grill":
stft = librosa.feature.inverse.mel_to_stft(spectrogram, sr=sr, n_fft=2048, power=2.0, fmin=80.0, fmax=16000)
return get_audio_from_spectrogram(stft, "stft", hop_length, sr)
elif feature == "nn_log_mel_grill":
mel = librosa.db_to_power(spectrogram) - np.ones(spectrogram.shape)
return get_audio_from_spectrogram(mel, "mel_grill", hop_length, sr)
else:
raise err.InvalidArgumentValueException("Unknown feature representation, can't reconstruct a signal.")
# %% Implementation of PCP from MSAF (for baseline comparison)
def get_pcp_as_msaf(signal, sr, hop_length):
audio_harmonic, _ = librosa.effects.hpss(y=signal)
pcp_cqt = np.abs(librosa.hybrid_cqt(y=audio_harmonic,
sr=sr,
hop_length=hop_length,
n_bins=84,
norm=np.inf,
fmin=27.5)) ** 2
pcp = librosa.feature.chroma_cqt(C=pcp_cqt,
sr=sr,
hop_length=hop_length,
n_octaves=6,
fmin=27.5).T
frame_times = librosa.core.frames_to_time(np.arange(pcp.shape[0]), sr, hop_length)
return pcp, frame_times
def get_beatsync_pcp_as_msaf(signal, sr, hop_length):
audio_harmonic, audio_percussive = librosa.effects.hpss(y=signal)
pcp_cqt = np.abs(librosa.hybrid_cqt(y=audio_harmonic,
sr=sr,
hop_length=hop_length,
n_bins=84,
norm=np.inf,
fmin=27.5)) ** 2
pcp = librosa.feature.chroma_cqt(C=pcp_cqt,
sr=sr,
hop_length=hop_length,
n_octaves=6,
fmin=27.5).T
frame_times = librosa.core.frames_to_time(np.arange(pcp.shape[0]), sr, hop_length)
# Compute beats
_, beat_frames = librosa.beat.beat_track(y=audio_percussive, sr=sr, hop_length=hop_length)
# To times
beat_times = librosa.frames_to_time(beat_frames, sr=sr,hop_length=hop_length)
# TODO: Is this really necessary?
if len(beat_times) > 0 and beat_times[0] == 0:
beat_times = beat_times[1:]
beat_frames = beat_frames[1:]
# Make beat synchronous
beatsync_feats = librosa.util.utils.sync(pcp.T, beat_frames, pad=True).T
# Assign times (and add last time if padded)
beatsync_times = np.copy(beat_times)
if beatsync_times.shape[0] != beatsync_feats.shape[0]:
beatsync_times = np.concatenate((beatsync_times,
[frame_times[-1]]))
return beatsync_feats, beatsync_times
\ No newline at end of file
as_seg/model/signal_to_spectrogram.py 0 → 100755
+ 188
− 0
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# -*- coding: utf-8 -*-
"""
Created on Wed Mar 25 16:54:59 2020
@author: amarmore
Computing spectrogram in different feature description.
Note that Mel (and variants of Mel) spectrograms follow the particular definition of [1].
[1] Grill, T., & Schlüter, J. (2015, October).
Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations.
In ISMIR (pp. 531-537).
"""
import numpy as np
import librosa.core
import librosa.feature
import librosa.effects
from math import inf
import as_seg.model.errors as err
import IPython.display as ipd
mel_power = 2
# TODO: add MFCC, maybe tonnetz
def get_spectrogram(signal, sr, feature, hop_length, fmin = 98):
"""
Returns a spectrogram, from the signal of a song.
Different types of spectrogram can be computed, which are specified by the argument "feature".
All these spectrograms are computed with the toolbox librosa [1].
Parameters
----------
signal : numpy array
Signal of the song.
sr : float
Sampling rate of the signal, (typically 44100Hz).
feature : String
The types of spectrograms to compute.
TODO
hop_length : integer
The desired hop_length, which is the step between two frames (ie the time "discretization" step)
It is expressed in terms of number of samples, which are defined by the sampling rate.
fmin : integer, optional
The minimal frequence to consider, used for denoising.
The default is 98.
n_mfcc : integer, optional
Number of mfcc features.
The default is 20 (as in librosa).
Raises
------
InvalidArgumentValueException
If the "feature" argument is not presented above.
Returns
-------
numpy array
Spectrogram of the signal.
References
----------
[1] McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., & Nieto, O. (2015, July).
librosa: Audio and music signal analysis in python.
In Proceedings of the 14th python in science conference (Vol. 8).
[2] Grill, T., & Schlüter, J. (2015, October).
Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations.
In ISMIR (pp. 531-537).
"""
if feature.lower() == "pcp":
return compute_pcp(signal, sr, hop_length, fmin)
elif feature.lower() == "cqt":
return compute_cqt(signal, sr, hop_length)
# For Mel spectrograms, we use the same parameters as the ones of [2].
# [2] Grill, Thomas, and Jan Schlüter. "Music Boundary Detection Using Neural Networks on Combined Features and Two-Level Annotations." ISMIR. 2015.
elif feature.lower() == "mel":
return compute_mel_spectrogram(signal, sr, hop_length)
elif "mel" in feature:
mel_spectrogram = get_spectrogram(signal, sr, "mel", hop_length)
return get_log_mel_from_mel(mel_spectrogram, feature)
elif feature.lower() == "stft":
return compute_stft(signal, sr, hop_length, complex = False)
elif feature.lower() == "stft_complex":
return compute_stft(signal, sr, hop_length, complex = True)
else:
raise err.InvalidArgumentValueException(f"Unknown signal representation: {feature}.")
def get_default_frequency_dimension(feature):
if feature.lower() == "pcp":
return 12
elif feature.lower() == "cqt":
return 84
elif "mel" in feature.lower():
return 80
elif feature.lower() == "stft" or feature.lower() == "stft_complex":
return 1025
else:
raise err.InvalidArgumentValueException(f"Unknown signal representation: {feature}.")
def compute_pcp(signal, sr, hop_length, fmin):
norm=inf # Columns normalization
win_len_smooth=82 # Size of the smoothign window
n_octaves=6
bins_per_chroma = 3
bins_per_octave=bins_per_chroma * 12
return librosa.feature.chroma_cens(y=signal,sr=sr,hop_length=hop_length,
fmin=fmin, n_chroma=12, n_octaves=n_octaves, bins_per_octave=bins_per_octave,
norm=norm, win_len_smooth=win_len_smooth)
def compute_cqt(signal, sr, hop_length):
constant_q_transf = librosa.cqt(y=signal, sr = sr, hop_length = hop_length)
return np.abs(constant_q_transf)
def compute_mel_spectrogram(signal, sr, hop_length):
mel = librosa.feature.melspectrogram(y=signal, sr = sr, n_fft=2048, hop_length = hop_length, n_mels=80, fmin=80.0, fmax=16000, power=mel_power)
return np.abs(mel)
def get_log_mel_from_mel(mel_spectrogram, feature):
"""
Computes a variant of a Mel spectrogram (typically Log Mel).
Parameters
----------
mel_spectrogram : numpy array
Mel spectrogram of the signal.
feature : string
Desired feature name (must be a variant of a Mel spectrogram).
Raises
------
err.InvalidArgumentValueException
Raised in case of unknown feature name.
Returns
-------
numpy array
Variant of the Mel spectrogram of the signal.
"""
if feature == "log_mel":
return librosa.power_to_db(np.abs(mel_spectrogram), ref=1)
elif feature == "nn_log_mel":
mel_plus_one = np.abs(mel_spectrogram) + np.ones(mel_spectrogram.shape)
nn_log_mel = librosa.power_to_db(mel_plus_one, ref=1)
return nn_log_mel
elif feature == "padded_log_mel":
log_mel = get_log_mel_from_mel(mel_spectrogram, "log_mel")
return log_mel - np.amin(log_mel) * np.ones(log_mel.shape)
elif feature == "minmax_log_mel":
padded_log_mel = get_log_mel_from_mel(mel_spectrogram, "padded_log_mel")
return np.divide(padded_log_mel, np.amax(padded_log_mel))
else:
raise err.InvalidArgumentValueException("Unknown feature representation.")
def compute_stft(signal, sr, hop_length, complex):
stft = librosa.stft(y=signal, hop_length=hop_length,n_fft=2048)
if complex:
mag, phase = librosa.magphase(stft, power = 1)
return mag, phase
else:
return np.abs(stft)
def get_stft_from_mel(mel_spectrogram, feature, sr):
if feature == "mel":
return librosa.feature.inverse.mel_to_stft(M=mel_spectrogram, sr=sr, n_fft=2048, power=mel_power, fmin=80.0, fmax=16000)
elif feature == "log_mel":
mel = librosa.db_to_power(S_db=mel_spectrogram, ref=1)
return get_stft_from_mel(mel, "mel", sr=sr)
elif feature == "nn_log_mel":
mel = librosa.db_to_power(S_db=mel_spectrogram, ref=1) - np.ones(mel_spectrogram.shape)
return get_stft_from_mel(mel, "mel", sr=sr)
else:
raise err.InvalidArgumentValueException("Unknown feature representation.")
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