-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathplots.py
108 lines (76 loc) · 3.45 KB
/
plots.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
import librosa
import librosa.display
import numpy as np
from matplotlib import pyplot as plt
from constants import *
def show_plots_compare_two_signals(signal_data, signal_data_reduced):
fig, ax = plt.subplots(nrows=2, sharex="all", sharey="all", constrained_layout=True)
ax[0].set(title="Original Audio Waveform Graph", xlabel=TXT_TIME, ylabel=TXT_AMPLITUDE)
ax[1].set(title="Audio Waveform Graph", xlabel=TXT_TIME, ylabel=TXT_AMPLITUDE)
# Apply grid
ax[0].grid()
ax[1].grid()
librosa.display.waveshow(signal_data, sr=DEFAULT_SAMPLE_RATE, ax=ax[0], label=TXT_ORIGINAL)
librosa.display.waveshow(signal_data, sr=DEFAULT_SAMPLE_RATE, ax=ax[1], label=TXT_ORIGINAL)
librosa.display.waveshow(signal_data_reduced, sr=DEFAULT_SAMPLE_RATE, ax=ax[1], label=TXT_FILTERED)
# Set legend
ax[1].legend()
# Show plot
plt.show()
# Save plot to directory
fig.savefig(".\\data\\plots\\original_and_filtered_audio.png")
def show_plot_emphasized(signal_data_orig, signal_data_emphasized):
s_orig = librosa.amplitude_to_db(np.abs(librosa.stft(signal_data_orig)), ref=np.max, top_db=None)
s_pre_emphasized = librosa.amplitude_to_db(np.abs(librosa.stft(signal_data_emphasized)), ref=np.max, top_db=None)
fig, ax = plt.subplots(nrows=2, sharex="all", sharey="all", constrained_layout=True)
librosa.display.specshow(s_orig, y_axis='log', x_axis='time', ax=ax[0])
img = librosa.display.specshow(s_pre_emphasized, y_axis='log', x_axis='time', ax=ax[1])
fig.colorbar(img, ax=ax, format="%+2.f dB")
ax[0].label_outer()
# Set title
ax[0].set(title=TXT_ORIGINAL_SIGNAL, xlabel=TXT_TIME, ylabel=TXT_FREQUENCY)
ax[1].set(title=TXT_PRE_EMPHASIZED_SIGNAL, xlabel=TXT_TIME, ylabel=TXT_FREQUENCY)
# Show plot
plt.show()
# Save plot to directory
fig.savefig(".\\data\\plots\\original_and_pre_emphasis.png")
def show_plot_zcr(signal_data_zcr):
plt.plot(signal_data_zcr[0])
# Set title
plt.title(TXT_ZERO_CROSSING_RATE)
# Apply grid
plt.grid()
# Save plot to directory
plt.savefig(".\\data\\plots\\zero_crossing_rate.png")
# Zooming in
plt.figure(figsize=(14, 5))
# Show plot
plt.show()
def show_plot_short_time_energy(signal_data_original, signal_data_ste):
time = np.arange(len(signal_data_original)) * (1.0 / DEFAULT_SAMPLE_RATE)
plt.figure()
plt.plot(time, signal_data_ste, 'm', linewidth=2)
plt.legend([TXT_ORIGINAL, TXT_STE])
plt.title(TXT_SHORT_TIME_ENERGY)
plt.xlabel(TXT_TIME)
# Save plot to directory
plt.savefig(".\\data\\plots\\short_time_energy.png")
# Show the plot
plt.show()
def show_mel_spectrogram(signal_nparray, num):
# Calculating the Short-Time Fourier Transform of signal
spectrogram = librosa.stft(signal_nparray)
# Using the mel-scale instead of raw frequency
spectrogram_mag, _ = librosa.magphase(spectrogram)
mel_scale_spectrogram = librosa.feature.melspectrogram(S=spectrogram_mag,
sr=DEFAULT_SAMPLE_RATE)
# use the decibel scale to get the final Mel Spectrogram
mel_spectrogram = librosa.amplitude_to_db(mel_scale_spectrogram, ref=np.min)
librosa.display.specshow(mel_spectrogram,
sr=DEFAULT_SAMPLE_RATE,
x_axis='time',
y_axis='mel')
plt.colorbar(format="%+2.0f dB")
# Zooming in
plt.figure(figsize=(14, 5))
plt.show()