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# -*- coding: utf-8 -*-
"""
================
Rainbowgrams
================
This notebook demonstrates how to use "Rainbowgrams" to simultaneously
visualize amplitude and (unwrapped) phase (differential) as demonstrated in the
`NSynth paper <https://proceedings.mlr.press/v70/engel17a/engel17a.pdf>`_ [1]_.
.. [1] Engel, Jesse, Cinjon Resnick, Adam Roberts, Sander Dieleman, Mohammad Norouzi, Douglas Eck, and Karen Simonyan.
"Neural audio synthesis of musical notes with wavenet autoencoders."
In International Conference on Machine Learning, pp. 1068-1077. PMLR, 2017.
"""
# Code source: Brian McFee
# License: ISC
#########################
# Standard imports
import numpy as np
import matplotlib.pyplot as plt
import librosa
#############################################
# We implemented a stft method to visualize the rainbowgram and demonstrated the result with a chirp signal.
# A chirp signal starts at a low frequency and gradually increases in frequency over time. We then separated the magnitude and phase components of the signal
sr = 22050
y = librosa.chirp(fmin=32, fmax=32 * 2**5, sr=sr, duration=10, linear=True)
D = librosa.stft(y)
mag, phase = librosa.magphase(D)
###########################################
# We should be visualizing the demodulated phase differential derived by subtracting 2π*f*t
# from each phase estimate prior to unwrapping, where f and t are the frequency and time.
freqs = librosa.fft_frequencies()
times = librosa.times_like(D)
phase_exp = 2*np.pi*np.multiply.outer(freqs,times)
####################
# Plot the spectrum.
fig, ax = plt.subplots()
img = librosa.display.specshow(np.diff(np.unwrap(np.angle(phase)-phase_exp, axis=1), axis=1, prepend=0),
cmap='hsv',
alpha=librosa.amplitude_to_db(mag, ref=np.max)/80 + 1,
ax=ax,
y_axis='log',
x_axis='time')
ax.set_facecolor('#000')
cbar = fig.colorbar(img, ticks=[-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
cbar.ax.set(yticklabels=['-π', '-π/2', "0", 'π/2', 'π']);
plt.show()
################################
# The above uses HSV colormap for phase fading to a black background. The twilight colormap
# can also work here, with the caveat that it uses black to code the extremes of the map (ie 0).
# We can sidestep this by using a neutral axis facecolor:
fig, ax = plt.subplots()
img = librosa.display.specshow(np.diff(np.unwrap(np.angle(phase)-phase_exp, axis=1), axis=1, prepend=0),
cmap='twilight',
alpha=librosa.amplitude_to_db(mag, ref=np.max)/80 + 1,
ax=ax,
y_axis='log',
x_axis='time')
ax.set_facecolor('#888')
cbar = fig.colorbar(img, ticks=[-np.pi, -np.pi/2, 0, np.pi/2, np.pi])
cbar.ax.set(yticklabels=['-π', '-π/2', 0, 'π/2', 'π'])
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