def stft_fast_rls_ld_coefs(X_1, X_2, d, filter_length, h):
# Retrieve useful information
n_frames = X_1.shape[1]
n_freqs = X_1.shape[0]
dtype = X_1.dtype
epsilon = np.zeros(X_1.shape, dtype=dtype)
x_2 = np.zeros((n_freqs, filter_length), dtype=dtype)
for m in np.arange(n_frames - 1) + 1:
helper.update_progress(m / n_frames)
print('Frame {}/{}'.format(m, n_frames))
for f in np.arange(n_freqs):
epsilon[f][m] = X_1[f][m]
if m >= d:
epsilon[f][m] -= np.transpose(np.conjugate(
2 * h[f][m - d])) @ x_2[f]
# If we still have not reached the last frame, refresh x_2
if m != n_frames - 1:
x_2 = np.roll(np.transpose(x_2), 1)
x_2[0] = X_2[:, m + 1]
x_2 = np.transpose(x_2)
helper.update_progress(1)
return epsilon
def stft_wiener_filter(X_1, X_2, alpha):
n_freqs = X_1.shape[0]
n_frames = X_1.shape[1]
# Init variables
r_0_1_prev = np.zeros(n_freqs, dtype=complex)
r_0_1 = np.zeros(n_freqs, dtype=complex)
r_0_prev = np.zeros(n_freqs, dtype=complex)
r_0 = np.zeros(n_freqs, dtype=complex)
h = np.zeros(n_freqs, dtype=complex)
output = np.zeros(X_1.shape, dtype=complex)
# Fill the output matrix
for m in np.arange(n_frames):
helper.update_progress(m / n_frames)
print('Frame {}/{}'.format(m, n_frames))
for f in range(X_1.shape[0]):
r_0_1[f] = alpha * X_2[f, m] * np.conj(
X_1[f, m]) + (1 - alpha) * r_0_1_prev[f]
r_0[f] = alpha * np.square(np.abs(
X_1[f, m])) + (1 - alpha) * r_0_prev[f]
if r_0[f] != 0:
h[f] = r_0_1[f] / r_0[f]
else:
print('Encountered a zero in the division')
output[:, m] = np.multiply(h, X_1[:, m])
r_0_1_prev = r_0_1
r_0_prev = r_0
helper.update_progress(1)
return output
def stft_fast_rls_def_coefs(X_1, X_2, d, filter_length, f_factor, delta):
# Retrieve useful information
n_frames = X_1.shape[1]
n_freqs = X_1.shape[0]
dtype = X_1.dtype
# Initialize variables
h = np.zeros((n_freqs, n_frames, filter_length, 1), dtype=dtype)
x_2 = np.zeros((n_freqs, filter_length), dtype=dtype)
P = np.zeros((n_frames, filter_length, filter_length), dtype=dtype)
P[0] = np.eye(filter_length, dtype=dtype) * (1 / delta)
# More initializations
E = np.zeros(X_1.shape, dtype=dtype)
g = np.zeros((n_frames, filter_length, 1), dtype=dtype)
epsilon = np.zeros(X_1.shape, dtype=dtype)
for m in np.arange(n_frames - 1) + 1:
helper.update_progress(m / n_frames)
print('Frame {}/{}'.format(m, n_frames))
for f in np.arange(n_freqs):
temp = np.reshape(
np.conj(x_2[f]).transpose() @ P[m - 1], (1, filter_length))
# A-priori error
E[f][m] = X_1[f][m] - np.transpose(np.conj(
2 * h[f][m - 1])) @ x_2[f]
# Kalmann gain vector
num = np.asarray(P[m - 1] @ x_2[f])
denom = np.asarray(f_factor + temp @ x_2[f])[0]
g[m] = np.reshape(num / denom, (filter_length, 1))
# Update
P[m] = (P[m - 1] - g[m] @ temp) / f_factor
h[f][m] = h[f][m - 1] + g[m] * np.conj(E[f][m])
# Output
epsilon[f][m] = X_1[f][m]
if m >= d:
epsilon[f][m] -= np.transpose(np.conjugate(
2 * h[f][m - d])) @ x_2[f]
# If we still have not reached the last frame, refresh x_2
if m != n_frames - 1:
x_2 = np.roll(np.transpose(x_2), 1)
x_2[0] = X_2[:, m + 1]
x_2 = np.transpose(x_2)
helper.update_progress(1)
return epsilon, h
def combine_lists(rank_list):
users = {}
no_users = rank_list[0].shape[0]
no_plots = len(rank_list)
for i in range(no_users):
user = rank_list[0][i].item(0)
users[user] = [i]
for j in range(1, no_plots):
users[user].append(
np.where((rank_list[j][:, 0] == user).all(axis=1))[0].item(0))
update_progress(i + 1, no_users)
score_list = []
for user in users.keys():
score = 0.0
for pos in users[user]:
score += 1 / float(60 + pos)
score_list.append((user, score))
return [x[0] for x in sorted(score_list, key=lambda t: t[1])]
