from scipy.io import wavfile
from scipy.interpolate import interp1d
import damage, recognize, utils, evaluate
import numpy as np
sample_rate, samples = wavfile.read('songs/hakuna_matata.wav')
samples = samples[1000000:2000000]
fp = [0, 0.05, 0.1, 0.2, 0.3]
fn = [0, 0.05, 0.1, 0.2, 0.3]
print('zerofill')
for p in fp:
for n in fn:
newsamples = samples.copy()
damage.zerofill(newsamples, 0.4)
matches = recognize.cheat(samples,
newsamples,
false_positives=p,
false_negatives=n)
validx, validy = utils.tovalidxy(newsamples, matches)
f = interp1d(validx, validy, kind='cubic', fill_value='extrapolate')
invalidx = utils.invalidx(matches)
fixedy = f(invalidx)
utils.replace(newsamples, invalidx, fixedy)
print('fp:', p, 'fn:', n, 'mean:',
np.mean(evaluate.abserrors(samples, newsamples)))
print('noiseadd')
for p in fp:
for n in fn:
#! /usr/bin/env python
from scipy.io import wavfile
from scipy.interpolate import interp1d
import damage, recognize, utils
sample_rate, samples = wavfile.read('songs/hakuna_matata.wav')
samples = samples[5000000:5000100]
newsamples = samples.copy()
damage.zerofill(newsamples, 0.5, blocksize=2)
matches = recognize.cheat(samples, newsamples)
x, y = utils.tovalidxy(newsamples, matches)
f = interp1d(x, y, kind='cubic', fill_value='extrapolate')
utils.repair(newsamples, matches, f)
import matplotlib.pyplot as plt
plt.title('Cubic interpolation')
plt.xlabel('Frame')
plt.ylabel('Amplitude')
plt.plot(samples, label='real')
plt.plot(newsamples, label='interpolated')
plt.legend(loc='best')
plt.show()