def fit2(kern, audio, file_name, max_par, fs):
"""Fit kernel to data """
# time vector for kernel
n = kern.size
xkern = np.linspace(0., (n - 1.) / fs, n).reshape(-1, )
# initialize parameters
if0 = gpitch.find_ideal_f0([file_name])[0]
init_f, init_v = gpitch.init_cparam(y=audio, fs=fs, maxh=max_par, ideal_f0=if0, scaled=False)[0:2]
list_init_params = []
for i in range(init_v.size):
list_init_params.append([init_v[i], 0.1, init_f[i]])
p0 = np.array(list_init_params).reshape(-1, )
# optimization
popt = scipy.optimize.curve_fit(func, xkern, kern.reshape(-1,), p0, bounds=(0., p0.size*[20000.]))[0]
# compute initial and learned kernel
kern_init = func(xkern, *p0)
kern_approx = func(xkern, *popt)
# get kernel hyperparameters
# npartials = (pstar.size - 2) / 2
# noise_var = pstar[0]
# lengthscale = pstar[1]
# variance = pstar[2: npartials + 2]
# frequency = pstar[npartials + 2:]
# params = [lengthscale, variance, frequency]
params = popt
return params, kern_init, kern_approx
def fit(kern, audio, file_name, max_par, fs):
"""Fit kernel to data """
# time vector for kernel
n = kern.size
xkern = np.linspace(0., (n - 1.) / fs, n).reshape(-1, 1)
# initialize parameters
if0 = gpitch.find_ideal_f0([file_name])[0]
init_f, init_v = gpitch.init_cparam(y=audio, fs=fs, maxh=max_par, ideal_f0=if0, scaled=False)[0:2]
init_l = np.array([0., 1.])
# optimization
p0 = np.hstack((init_l, init_v, init_f)) # initialize params
pstar = optimize_kern(x=xkern, y=kern, p0=p0)
# compute initial and learned kernel
kern_init = approximate_kernel(p0, xkern)
kern_approx = approximate_kernel(pstar, xkern)
# get kernel hyperparameters
npartials = (pstar.size - 2) / 2
noise_var = pstar[0]
lengthscale = pstar[1]
variance = pstar[2: npartials + 2]
frequency = pstar[npartials + 2:]
params = [lengthscale, variance, frequency]
return params, kern_init, kern_approx
def init_kernel(self, covsize=441, num_sam=10000, max_par=1, train=False, save=False, load=False):
nfiles = len(self.train_data)
self.params = [[], [], []]
skern, xkern = nfiles * [np.zeros((1, 1))], nfiles * [None]
if train:
scov, samples = nfiles * [None], nfiles * [None]
self.sampled_cov = nfiles * [None]
for i in range(nfiles):
# sample cov matrix
self.sampled_cov[i], skern[i], samples[i] = gpitch.samplecov.get_cov(self.train_data[i].y,
num_sam=num_sam, size=covsize)
# approx kernel
params = gpitch.kernelfit.fit(kern=skern[i], audio=self.train_data[i].y,
file_name=self.train_data[i].name, max_par=max_par, fs=16000)[0]
self.params[0].append(params[0]) # lengthscale
self.params[1].append(params[1]) # variances
self.params[2].append(params[2]) # frequencies
xkern[i] = np.linspace(0., (covsize - 1.) / self.train_data[i].fs, covsize).reshape(-1, 1)
self.kern_sampled = [xkern, skern]
if save:
self.save()
elif load:
self.load_kernel() # load already learned parameters
else:
# init kernels with fft of data
for i in range(nfiles):
f0 = gpitch.find_ideal_f0([self.train_data[i].name])[0]
params = gpitch.init_cparam(y=self.train_data[i].y.copy(),
fs=self.train_data[i].fs,
maxh=max_par,
ideal_f0=f0)
self.params[0].append(np.array(0.1)) # lengthscale
self.params[1].append(params[1]) # variances
self.params[2].append(params[0]) # frequencies
skern[i] = fftpack.ifft(np.abs(fftpack.fft(self.train_data[i].y.copy().reshape(-1, ))))[0:covsize].real
skern[i] /= np.max(skern[i])
xkern[i] = np.linspace(0., (covsize - 1.) / self.train_data[i].fs, covsize).reshape(-1, 1)
self.kern_sampled = [xkern, skern]
# init kernel specific pitch
self.kern_pitches = gpitch.init_kernels.init_kern_com(num_pitches=len(self.train_data),
lengthscale=self.params[0],
energy=self.params[1],
frequency=self.params[2],
len_fixed=True)
def get_kernel_features(filenames, ytrain, maxh, fs):
num_pitches = len(filenames)
if0 = gpitch.find_ideal_f0(filenames) # ideal frequency for each pitch
all = [gpitch.init_cparam(y=ytrain[i], fs=fs, maxh=maxh, ideal_f0=if0[i], scaled=False) for i in range(num_pitches)]
freq_feat = num_pitches*[None]
var_feat = num_pitches*[None]
for i in range(num_pitches):
freq_feat[i] = all[i][0].copy()
var_feat[i] = all[i][1].copy()
return all, freq_feat, var_feat
def sousep(gpu='0',
ins_idx=0,
start=0,
frames=14 * 16000,
window_size=800,
minibatch_size=None,
maxiter=5000,
nfpc=1,
use_centers=True,
visualize_results=True,
save=True):
if visualize_results:
import matplotlib.pyplot as plt
plt.rcParams["figure.figsize"] = (16, 4)
import gpitch.myplots as mplt
# initialize settings
sess = gpitch.init_settings(gpu) # select gpu
list_inst = ['011PFNOM', '131EGLPM', '311CLNOM',
'ALVARADO'] # list of instruments
inst = list_inst[ins_idx] # instrument to analyse
# print specifications of experiment
print("Analysing file {}, window size {}, ".format(inst, window_size) +
"GPU " + gpu)
print("Iterations {}, minibatch size {}".format(maxiter, minibatch_size))
# directories to load data and save results
testdata_directory = '/import/c4dm-04/alvarado/datasets/ss_amt/test_data/'
traindata_directory = '/import/c4dm-04/alvarado/datasets/ss_amt/training_data/'
save_location = "/import/c4dm-04/alvarado/results/ss_amt/evaluation/sousep/"
# load test data
test_file = inst + "_mixture.wav"
x, y, fs = gpitch.readaudio(testdata_directory + test_file,
frames=frames,
start=start,
scaled=True)
num_windows = y.size / window_size
print("Number of windows to analyze {}".format(num_windows))
xtest, ytest = gpitch.segmented(x, y, window_size=window_size)
# load train data for getting kernel features
train_files = gpitch.lfiles_training[ins_idx]
num_pitches = len(train_files)
if0 = gpitch.find_ideal_f0(train_files) # ideal frequency for each pitch
# init lists to save features
aux_list = num_pitches * [None]
xtrain = list(aux_list)
ytrain = list(aux_list)
f_center = list(aux_list)
v_center = list(aux_list)
f_vec = list(aux_list)
s_vec = list(aux_list)
th = list(aux_list)
frequency = list(aux_list)
energy = list(aux_list)
maxh = 25
totalnumf = maxh * nfpc
for i in range(num_pitches):
# load train data
xtrain[i], ytrain[i], fs = gpitch.readaudio(traindata_directory +
train_files[i])
# get kernel features
f_center[i], v_center[i], f_vec[i], s_vec[i], th[
i] = gpitch.init_cparam(y=ytrain[i],
fs=fs,
maxh=maxh,
ideal_f0=if0[i],
scaled=False)
frequency[i], energy[i] = gpitch.get_features(f=f_vec[i],
s=s_vec[i],
f_centers=f_center[i],
nfpc=nfpc,
use_centers=use_centers,
totalnumf=totalnumf)
# initialization of models
z, m, kern = [], [], []
for i in range(num_windows):
# init kernel
kern.append(
gpitch.init_kern(num_pitches=num_pitches,
energy=energy,
frequency=frequency))
# init inducing variables
z.append(
gpitch.init_liv(x=xtest[i].copy(),
y=ytest[i].copy(),
num_sources=num_pitches)[0])
# init model
m.append(
gpitch.pdgp.Pdgp(x=xtest[i],
y=ytest[i],
z=z[i],
kern=kern[i],
minibatch_size=minibatch_size))
m[i].za.fixed = True
m[i].zc.fixed = True
m[i].likelihood.variance = 1.
# optimization
results = []
for i in range(num_windows):
start_time = time.time()
m[i].optimize(disp=1, maxiter=maxiter)
m[i].kern_act.fixed = True
m[i].kern_com.fixed = True
m[i].likelihood.variance = 0.000001
m[i].optimize(disp=1, maxiter=maxiter)
# compute prediction
results.append(
gpitch.pdgp.predict_windowed(model=m[i],
xnew=xtest[i],
ws=window_size))
m[i].save_prediction = list(results[i])
print("Time optimizing and predicting {} secs".format(time.time() -
start_time))
# save models
if num_windows == 1:
pickle.dump(
m[i],
open(save_location + "models/" + inst + "_full_window.p",
"wb"))
else:
pickle.dump(
m[i],
open(
save_location + "models/" + inst + "_window_" +
str(i + 1) + ".p", "wb"))
# reset tensorflow graph
tf.reset_default_graph()
# merge results
results_merged = gpitch.window_overlap.merge_all(results)
x_final, y_final, r_final = gpitch.window_overlap.get_results_arrays_noov(
x=xtest, y=ytest, results=results_merged, window_size=window_size)
# save wav files
pitch_name = ['C', 'E', 'G']
if save:
for i in range(3):
#if num_windows == 1:
# name = inst + "_" + pitch_name[i] + "_part" + "_full_window.wav"
#else:
name = inst + "_" + pitch_name[i] + "_part.wav"
print name
aux = r_final[-1][i] / np.max(np.abs(r_final[-1][i]))
soundfile.write(save_location + name, aux, fs)
# visualize results
if visualize_results:
# plot spectral respresentation training data and selected features
plt.figure(figsize=(16, 9))
for i in range(num_pitches):
plt.subplot(3, 1, i + 1)
plt.plot(f_vec[i], s_vec[i] / np.max(s_vec[i]), 'xC0')
plt.plot(frequency[i], energy[i] / np.max(energy[i]), 'sC1')
plt.plot(f_center[i], v_center[i] / np.max(v_center[i]), 'vC2')
plt.legend([
"Spetral density data", "Features selected",
"Frequency centers"
])
# plot prediction components and activations
plt.figure(figsize=(16, 9))
for i in range(num_windows):
m_a, v_a, m_c, v_c, esource = m[i].save_prediction
for j in range(num_pitches):
plt.subplot(3, 2, 2 * (j + 1) - 1)
mplt.plot_predict(xtest[i],
m_a[j],
v_a[j],
m[i].za[j].value,
plot_z=True,
latent=True,
plot_latent=False)
plt.subplot(3, 2, 2 * (j + 1))
mplt.plot_predict(xtest[i],
m_c[j],
v_c[j],
m[i].zc[j].value,
plot_z=False)
# plot sources
plt.figure(figsize=(16, 9))
gpitch.window_overlap.plot_sources(x_final, y_final, r_final[-1])
sess.close()
return m, r_final[-1]