def akimaInterpolation():
sample_rate, sample = wavfile.read('songs/hakuna_matata.wav')
sample = sample[5000000:5000100]
sample_rateBAD, sampleBAD = wavfile.read(
'songs/bad_songs/not_good_song.wav')
sampleBAD = sampleBAD[5000000:5000100]
BadSample = sample.copy()
dz.theEvilMethod(BadSample, 0.5)
matches = recognize.cheat(sample, BadSample)
x, y = utils.tovalidxy(BadSample, matches)
f = Akima1DInterpolator(x, y)
IwannaSee(sample, BadSample, sampleBAD)
def cubicSplineInterpolation():
sample_rate, sample = wavfile.read('songs/hakuna_matata.wav')
BadSample = sample.copy()
dz.theEvilMethod(BadSample, 0.5)
matches = recognize.cheat(sample, BadSample)
x, y = utils.tovalidxy(BadSample, matches)
f = InterpolatedUnivariateSpline(x, y)
xNotValid = utils.invalidx(matches)
fixedy = f(xNotValid)
utils.replace(BadSample, xNotValid, fixedy)
wavfile.write('songs/generator_song/regen_splineUnivariate_song.wav',
sample_rate, BadSample)
def cubitInterpolation1D():
sample_rate, sample = wavfile.read('songs/hakuna_matata.wav')
BadSample = sample.copy()
dz.theEvilMethod(BadSample, 0.5)
wavfile.write('songs/bad_songs/not_good_song.wav', sample_rate, BadSample)
matches = recognize.cheat(sample, BadSample)
x, y = utils.tovalidxy(BadSample, matches)
f = interp1d(x, y, kind='cubic', fill_value='extrapolate')
xNotValid = utils.invalidx(matches)
fixedy = f(xNotValid)
utils.replace(BadSample, xNotValid, fixedy)
wavfile.write('songs/generator_song/regen_sinOriginal_song.wav',
sample_rate, BadSample)
def lagrangeInterpolation():
sample_rate, sample = wavfile.read('songs/hakuna_matata.wav')
sample = sample[5000000:5000100]
sample_rateBAD, sampleBAD = wavfile.read(
'songs/bad_songs/not_good_song.wav')
sampleBAD = sampleBAD[5000000:5000100]
BadSample = sample.copy()
dz.theEvilMethod(BadSample, 0.7)
matches = recognize.cheat(sample, BadSample)
x, y = utils.tovalidxy(BadSample, matches)
f = BarycentricInterpolator(x, y)
utils.repair(BadSample, matches, f)
IwannaSee(sample, BadSample, sampleBAD)
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:
newsamples = samples.copy()
damage.noiseadd(newsamples, 0.6, rate=0.3)
matches = recognize.cheat(samples,
newsamples,
false_positives=p,
#! /usr/bin/env python
from scipy.io import wavfile
from interpolate import NewtonInterpolator
import damage, recognize, utils
sample_rate, samples = wavfile.read('songs/hakuna_matata.wav')
samples = samples[5000000:5000100]
newsamples = samples.copy()
damage.zerofill(newsamples, 0.3)
matches = recognize.cheat(samples, newsamples)
x, y = utils.tovalidxy(newsamples, matches)
f = NewtonInterpolator(x, y)
utils.repair(newsamples, matches, f)
import matplotlib.pyplot as plt
plt.title('Newton interpolation')
plt.xlabel('Frame')
plt.ylabel('Amplitude')
plt.plot(samples, label='real')
plt.plot(newsamples, label='interpolated')
plt.legend(loc='best')
plt.show()
from scipy.interpolate import interp1d
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.metrics import accuracy_score
samplerate, samples = wavfile.read('canciones/hakuna_matata.wav')
samples = samples[5000000:5000100]
newsamples = samples.copy()
damage.noiseadd(newsamples, 0.7, 0.3)
matches = recognize.cheat(samples,
newsamples,
false_positives=0.04,
false_negatives=0.1)
matchesSD = recognize.cheat(samples,
samples,
false_positives=0.04,
false_negatives=0.1)
x, y = utils.tovalidxy(newsamples, matches)
xSD, ySD = utils.tovalidxy(samples, matchesSD)
x = np.array(x).reshape((-1, 1))
y = np.array(y)
xSD = np.array(xSD).reshape((-1, 1))
ySD = np.array(ySD)
xP, yP, xSD, ySD = utils.partir(x, y, xSD, ySD, 10)
polynomial_features = PolynomialFeatures(degree=10)
y_poly_pred = utils.polinomialR(xP, yP, ySD, polynomial_features)
utils.plotting(xP, yP, samples, y_poly_pred)