def main():
song_name = '2 - The Box.wav'
sample_path = wav_dir2 + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
x = transformer.calculate_transform(signal, sr, 'melspectrogram', 1024)
x = np.transpose(x)
x[:, 7:57] = 0
x[:, 815:865] = 0
x[:, 1636:1686] = 0
x[:, 3277:3327] = 0
x[:, 4918:4968] = 0
x[:, 8200:8250] = 0
x[:, 9841:9891] = 0
x[:, 11482:11532] = 0
x[:, 14764:14814] = 0
x[:, 16405:16455] = 0
x[:, 18047:18097] = 0
plt.figure(figsize=(7, 4))
# ax_img = plt.imshow(x[:, :3000], origin='lower')
# plt.savefig(visuals_dir + song_name.replace('.wav', '.png'))
ax_img = librosa.display.specshow(x,
cmap='viridis',
x_axis='time',
y_axis='mel',
sr=sr,
fmax=sr // 2)
plt.colorbar(format='%+2.0f dB')
plt.title(
'The Box by Roddy Ricch - Mel-spectrogram with all Points of Interest')
plt.show()
def main():
song_name = '04 - Thriller.wav'
sample_path = wav_dir + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
z = transformer.calculate_transform(signal, sr, 'melspectrogram', 1024)
z = np.transpose(z)
z[:, 21774:21776] = -9
x = np.arange(215, 220, 0.01)
y = np.arange(0, 44100, 44100 / 1024)
x, y = np.meshgrid(x, y)
z = z[:, 21500:22000]
plt.subplot()
plt.pcolor(x, y, z, cmap='viridis', vmin=-100, vmax=-9)
plt.axis([x.min(), x.max(), y.min(), y.max()])
plt.colorbar()
plt.xlabel('Time (seconds)')
plt.ylabel('Frequency (Hz)')
plt.title(
'Thriller by Michael Jackson - Mel-Spectrogram with a Point of Interest'
)
plt.show()
def main():
song_name = '04 - Thriller.wav'
sample_path = wav_dir + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
z = transformer.calculate_transform(signal, sr, 'melspectrogram', 1024)
z = np.transpose(z)
z[:, 2127:2177] = -9
z[:, 3746:3786] = -9
z[:, 9023:9073] = -9
z[:, 10643:10683] = -9
z[:, 14692:14742] = -9
z[:, 16312:16362] = -9
z[:, 21774:21824] = -9
z[:, 23393:23443] = -9
z[:, 23797:23847] = -9
z[:, 25416:25456] = -9
z[:, 31885:31935] = -9
z[:, 34312:34362] = -9
"""
x = np.arange(0, 357.65, 0.01)
y = np.arange(0, 44100, 44100 / 1024)
x, y = np.meshgrid(x, y)
plt.subplot()
plt.pcolor(x, y, z, cmap='viridis', vmin=-100, vmax=-9)
plt.axis([x.min(), x.max(), y.min(), y.max()])
plt.colorbar()
plt.xlabel('Time (seconds)')
plt.ylabel('Frequency (Hz)')
"""
librosa.display.specshow(z,
cmap='viridis',
x_axis='time',
y_axis='mel',
sr=sr,
fmax=sr // 2)
plt.colorbar(format='%+2.0f dB')
plt.title(
'Thriller by Michael Jackson - Mel-Spectrogram with all Points of Interest'
)
plt.show()
def main():
song_name = '2 - The Box.wav'
#song_name = '02 EARFQUAKE.wav'
#song_name = 'JACKBOYS - OUT WEST.wav'
sample_path = wav_dir2 + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
x = transformer.calculate_transform(signal, sr, 'mfcc', 12)
x = np.transpose(x)
plt.figure(figsize=(7, 4))
ax_img = librosa.display.specshow(x, cmap='viridis', x_axis='time', sr=sr)
plt.colorbar()
plt.title('The Box by Roddy Ricch - Mel-Frequency Cepstral Coefficients')
#plt.title('EARFQUAKE by Tyler, The Creator - Mel-Frequency Cepstral Coefficients')
#plt.title('OUT WEST by Jack Boys - Mel-Frequency Cepstral Coefficients')
plt.show()
def main():
song_name = '(03) [boney M] Daddy Cool.wav'
#song_name = '2 - The Box.wav'
#song_name = '02 EARFQUAKE.wav'
#song_name = 'JACKBOYS - OUT WEST.wav'
sample_path = wav_dir3 + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
x = transformer.calculate_transform(signal, sr, 'spectrogram', 512, 10)
plt.figure(figsize=(7, 4))
librosa.display.specshow(x[:, :1500],
cmap='viridis',
x_axis='time',
y_axis='mel',
sr=sr,
fmax=sr // 2)
plt.colorbar(format='%+2.0f dB')
# plt.title('The Box by Roddy Ricch - Mel-Spectrogram')
plt.title(song_name)
plt.show()
def main():
#song_name = '2 - The Box.wav'
song_name = '02 EARFQUAKE.wav'
song_name = 'JACKBOYS - OUT WEST.wav'
sample_path = wav_dir2 + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
x = transformer.calculate_transform(signal, sr, 'spectrogram', 1024)
x = np.transpose(x)
plt.figure(figsize=(7, 4))
librosa.display.specshow(x[:, :1500],
cmap='viridis',
x_axis='time',
y_axis='hz',
sr=sr,
fmax=sr // 2)
plt.colorbar(format='%+2.0f dB')
#plt.title('The Box by Roddy Ricch - Spectrogram')
#plt.title('EARFQUAKE by Tyler, The Creator - Spectrogram')
plt.title('OUT WEST by Jack Boys - Spectrogram')
plt.show()
def main():
#song_name = '2 - The Box.wav'
song_name = '02 EARFQUAKE.wav'
song_name = 'JACKBOYS - OUT WEST.wav'
sample_path = wav_dir2 + song_name
transformer = Transform()
signal, sr = librosa.load(sample_path, sr=44100, mono=True)
x = transformer.calculate_transform(signal, sr, 'cqt', 84)
x = np.transpose(x)
plt.figure(figsize=(7, 4))
#librosa.display.specshow(x[:, 861: 3364], cmap='viridis', x_axis='time', y_axis='cqt_note', sr=39785)
librosa.display.specshow(x[:, :1292],
cmap='viridis',
x_axis='time',
y_axis='cqt_note',
sr=39785)
plt.colorbar(format='%+2.0f dB')
#plt.title('EARFQUAKE by Tyler, The Creator - Constant-Q')
#plt.title('The Box by Roddy Ricch - Constant-Q')
plt.title('OUT WEST by Jack Boys - Constant-Q')
plt.show()
from basic_classes.constants import wav_dir
import os
from basic_classes.transforms import Transform
import librosa
from .helper import load_transform
import numpy as np
n_feats = 512
transform_type = 'spectrogram'
ws = 10
spec_dir = 'D:/Documents/Thesis/Saved Transforms/Images/Spectrograms/' + str(
ws) + '/' + str(n_feats) + '/'
wav_list = os.listdir(wav_dir)
transformer = Transform()
wav_name = wav_list[0]
signal, sr = librosa.load(wav_dir + wav_name)
print('• Creating spectrogram of ' + wav_name)
"""
transform = transformer.calculate_transform(samples=signal,
sample_rate=sr,
n_feats=n_feats,
transform_type=transform_type,
window_size=ws)
"""
spec_name = wav_name.replace('.wav', '.png')
loaded_transform = load_transform(spec_dir + spec_name)
from basic_classes.constants import wav_dir
import os
from basic_classes.transforms import Transform
import librosa
from .helper import load_transform
import numpy as np
import skimage
n_feats = 512
transform_type = 'spectrogram'
ws = 10
spec_dir = 'D:/Documents/Thesis/Saved Transforms/Images/Spectrograms/' + str(
ws) + '/' + str(n_feats) + '/'
wav_list = os.listdir(wav_dir)
transformer = Transform()
wav_name = wav_list[0]
signal, sr = librosa.load(wav_dir + wav_name)
print('• Creating spectrogram of ' + wav_name)
transform = transformer.calculate_transform(samples=signal,
sample_rate=sr,
n_feats=n_feats,
transform_type=transform_type,
window_size=ws)
spec_name = wav_name.replace('.wav', '.png')
transform = skimage.util.img_as_ubyte(transform)
print(transform.shape)