Spaces:
Sleeping
Sleeping
File size: 5,363 Bytes
6dea0da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import torch
import numpy as np
def smooth_LTAS(LTAS, f, Noct=1):
# based on https://github.com/IoSR-Surrey/MatlabToolbox/blob/4bff1bb2da7c95de0ce2713e7c710a0afa70c705/%2Biosr/%2Bdsp/smoothSpectrum.m
def gauss_f(f_x, F, Noct):
sigma = (F / Noct) / np.pi
g = torch.exp(-(((f_x - F)**2) / (2 * (sigma**2))))
g = g / torch.sum(g)
return g
x_oct = LTAS.clone()
if Noct > 0:
for i in range(1, len(f)):
g = gauss_f(f, f[i], Noct)
g = g.to(LTAS.device)
x_oct[i] = torch.sum(g * LTAS)
if torch.all(LTAS >= 0):
x_oct[x_oct < 0] = 0
return x_oct
def compute_LTAS(x, sample_rate, nfft=2048, hop_length=512, win_length=2048, normalize=None, sqrt=False):
"""
Long-term average spectrum of a single waveform already at sample_rate.
"""
if len(x.shape) == 2:
x = np.mean(x, axis=1)
x = torch.tensor(x, dtype=torch.float32)
if normalize is not None:
std = x.std()
x = normalize * x / x.std()
else:
std = 1
X = torch.stft(x, n_fft=nfft, hop_length=hop_length, win_length=win_length, window=torch.hann_window(win_length), return_complex=True) / torch.sqrt(torch.hann_window(win_length).sum())
if sqrt:
Xsum = torch.sqrt(torch.sum(torch.abs(X)**2, dim=1).unsqueeze(-1))
else:
Xsum = torch.sum(torch.abs(X)**2, dim=1).unsqueeze(-1)
L = X.shape[-1]
X_norm = Xsum / L
X_mean = torch.mean(X_norm, dim=-1)
return X_mean, std
def apply_stft(x, NFFT):
window = torch.hamming_window(window_length=NFFT)
window = window.to(x.device)
x = torch.cat((x, torch.zeros(*x.shape[:-1], NFFT).to(x.device)), 1)
X = torch.stft(x, NFFT, hop_length=NFFT // 2, window=window, center=False, onesided=True, return_complex=True)
X = torch.view_as_real(X)
return X
def apply_filter_istft(X, H, NFFT):
window = torch.hamming_window(window_length=NFFT)
window = window.to(X.device)
X = X * H.unsqueeze(-1).unsqueeze(-1).expand(X.shape)
X = torch.view_as_complex(X)
x = torch.istft(X, NFFT, hop_length=NFFT // 2, window=window, center=False, return_complex=False)
return x
def apply_filter(x, H, NFFT):
X = apply_stft(x, NFFT)
xrec = apply_filter_istft(X, H, NFFT)
xrec = xrec[:, :x.shape[-1]]
return xrec
def design_filter_3(params, f, block_low_freq=False):
"""
Parametric shelving/EQ filter defined by a reference frequency (fref) and
piecewise log-linear slopes above (fc_p/A_p) and below (fc_m/A_m) it.
"""
fref = params[0]
fc_p = params[1]
fc_m = params[2]
A_p = params[3]
A_m = params[4]
assert (fc_p <= fref).any() == False, f"fc_p must be greater than fref: {fc_p}, {fref}"
assert (fc_m >= fref).any() == False, f"fc_m must be smaller than fre: {fc_m}, {fref}"
assert (fc_m <= f[1]).any() == False, f"fc_m must be greater than the minimum frequency: {fc_m}, {f[1]}"
assert (fc_p >= f[-1]).any() == False, f"fc_p must be smaller than the maximum frequency: {fc_p}, {f[-1]}"
f = f[1:]
H = torch.ones(f.shape).to(f.device)
H[f >= fref] = 10**(A_p[0] * torch.log2(f[f >= fref] / fref) / 20)
for i in range(0, len(fc_p)):
H[f >= fc_p[i]] = 10**(A_p[i + 1] * torch.log2(f[f >= fc_p[i]] / fc_p[i]) / 20) * H[f >= fc_p[i]][0]
if not block_low_freq:
H[f < fref] = 10**(A_m[0] * torch.log2(f[f < fref] / fref) / 20) * H[f < fref][-1]
for i in range(0, len(fc_m)):
H[f < fc_m[i]] = 10**(A_m[i + 1] * torch.log2(f[f < fc_m[i]] / fc_m[i]) / 20) * H[f < fc_m[i]][-1]
H = torch.cat((torch.zeros(1).to(f.device), H), 0)
return H
def apply_filter_and_norm_STFTmag_fweighted(X, Xref, H, freq_weight="linear"):
# X: (N,513, T) denoised estimate STFT, Xref: (N,513, T) observation STFT, H: (513,) filter
X = torch.sqrt(X[..., 0]**2 + X[..., 1]**2)
Xref = torch.sqrt(Xref[..., 0]**2 + Xref[..., 1]**2)
X = X * H.unsqueeze(-1).expand(X.shape)
freqs = torch.linspace(0, 1, X.shape[1]).to(X.device)
if freq_weight == "linear":
X = X * freqs.unsqueeze(-1).expand(X.shape)
Xref = Xref * freqs.unsqueeze(-1).expand(Xref.shape)
elif freq_weight == "None":
pass
elif freq_weight == "log":
X = X * torch.log2(1 + freqs.unsqueeze(-1).expand(X.shape))
Xref = Xref * torch.log2(1 + freqs.unsqueeze(-1).expand(Xref.shape))
elif freq_weight == "sqrt":
X = X * torch.sqrt(freqs.unsqueeze(-1).expand(X.shape))
Xref = Xref * torch.sqrt(freqs.unsqueeze(-1).expand(Xref.shape))
elif freq_weight == "log2":
X = X * torch.log2(freqs.unsqueeze(-1).expand(X.shape))
Xref = Xref * torch.log2(freqs.unsqueeze(-1).expand(Xref.shape))
elif freq_weight == "log10":
X = X * torch.log10(freqs.unsqueeze(-1).expand(X.shape))
Xref = Xref * torch.log10(freqs.unsqueeze(-1).expand(Xref.shape))
elif freq_weight == "cubic":
X = X * freqs.unsqueeze(-1).expand(X.shape)**3
Xref = Xref * freqs.unsqueeze(-1).expand(Xref.shape)**3
elif freq_weight == "quadratic":
X = X * freqs.unsqueeze(-1).expand(X.shape)**2
Xref = Xref * freqs.unsqueeze(-1).expand(Xref.shape)**2
norm = torch.linalg.norm(X.reshape(-1) - Xref.reshape(-1), ord=2)
return norm
|