fig.suptitle("Multi-pitch estimation using Klapuri's iterative method",
fontsize=16)
ax.set_title("Piano roll of Beethoven's \"Für Elise\"")
for m in range(pitch.shape[0]):
ax.scatter(time, pitch[m])
ax.set_xlabel('Time (s)')
ax.set_ylabel('Pitch (MIDI)')
ax.set_ylim(20, 109)
plt.show()
if __name__ == '__main__':
if len(sys.argv) > 3:
print("Wrong number of arguments", file=sys.stderr)
print("Usage: python %s [filename] [duration_in_seconds]" %
sys.argv[0],
file=sys.stderr)
sys.exit(1)
elif len(sys.argv) > 1:
audio_file = sys.argv[1]
audio = AudioLoader(audio_file)
if len(sys.argv) == 3:
audio.cut(stop=sys.argv[2])
else:
audio_file = "../samples/polyphonic/furElise.wav"
audio = AudioLoader(audio_file)
audio.cut(stop=10)
print("Using first ", 10, " seconds of ", audio_file)
demo_klapuri(audio)
from muallef.util.units import convertFreq, Hz_to_MIDI
from muallef.util import normalize
from matplotlib import pyplot as plt
from sys import argv
import numpy as np
if len(argv) == 2:
audio_file = argv[1]
else:
#audio_file = "samples/polyphonic/furElise.wav"
#audio_file = 'samples/polyphonic/chopin_cut.wav'
audio_file = 'samples/monophonic/czardas_cut.wav'
audio = AudioLoader(audio_file)
audio.cut(stop=10)
fs = audio.sampleRate
x = audio.signal
yin = MonoPitch(x, fs, method='yin')
yin_f0 = yin()
yin_conf = yin.get_confidence(normalize=True)
yinfft = MonoPitch(x, fs, method='yinfft')
yinfft_f0 = yinfft()
yinfft_conf = yinfft.get_confidence(normalize=True)
time = audio.time(len(yin_f0))
fig, ax = plt.subplots()
ax.scatter(time, yinfft_f0, c='red', s=10*yinfft_conf)
ax.scatter(time, yin_f0, c='blue', s=10*yin_conf)
from muallef.io import AudioLoader
from muallef.pitch import MultiPitch
from muallef.util.units import Hz_to_MIDI
import numpy as np
import matplotlib.pyplot as plt
# matplotlib options
from matplotlib import rcParams
rcParams['savefig.transparent'] = True
rcParams['text.usetex'] = True
# load audio
audio_file = "samples/polyphonic/furElise.wav"
audio = AudioLoader(audio_file)
audio.cut(stop=10)
fs = audio.sampleRate
x = audio.signal
frameSize = 2048
klapuri = MultiPitch(x, fs, method='klapuri', frameSize=frameSize)
pitch = Hz_to_MIDI(klapuri())
time = np.arange(pitch.shape[1]) * (frameSize / fs)
fig, ax = plt.subplots()
fig.suptitle("Multi-pitch estimation using Klapuri's iterative method", fontsize=16)
ax.set_title("Piano roll of Beethoven's \"Für Elise\"")
for m in range(pitch.shape[0]):
ax.scatter(time, pitch[m])
import matplotlib.pyplot as plt
# matplotlib options
from matplotlib import rcParams
rcParams['savefig.transparent'] = True
rcParams['text.usetex'] = True
method = {
'HFC': 'hfc',
'Complex': 'complex',
'Phase Deviation': 'complex_phase',
}
# load audio
audio_file = "samples/polyphonic/furElise.wav"
audio = AudioLoader(audio_file)
audio.cut(stop=10)
fs = audio.sampleRate
x = audio.signal
t = audio.time()
fig, ax = plt.subplots(len(method) + 1, 1, sharex=True)
i = 0
ax[i].plot(t, x)
ax[i].yaxis.set_ticklabels([])
ax[i].set_ylabel("Signal")
for key in method.keys():
i += 1
onset = Onset(x, fs, method=method[key])
onsets = onset()
ax[i].plot(onset.onsetTime, onset.onsetFunction)