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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
import torch
from torch import nn
from torch.nn import functional as F
def kaiser_attenuation(n_taps, f_h, sr):
df = (2 * f_h) / (sr / 2)
return 2.285 * (n_taps - 1) * math.pi * df + 7.95
def kaiser_beta(n_taps, f_h, sr):
atten = kaiser_attenuation(n_taps, f_h, sr)
if atten > 50:
return 0.1102 * (atten - 8.7)
elif 50 >= atten >= 21:
return 0.5842 * (atten - 21) ** 0.4 + 0.07886 * (atten - 21)
else:
return 0.0
def sinc(x, eps=1e-10):
y = torch.sin(math.pi * x) / (math.pi * x + eps)
y = y.masked_fill(x.eq(0), 1.0)
return y
def kaiser_window(n_taps, f_h, sr):
beta = kaiser_beta(n_taps, f_h, sr)
ind = torch.arange(n_taps) - (n_taps - 1) / 2
return torch.i0(beta * torch.sqrt(1 - ((2 * ind) / (n_taps - 1)) ** 2)) / torch.i0(
torch.tensor(beta)
)
def lowpass_filter(n_taps, cutoff, band_half, sr):
window = kaiser_window(n_taps, band_half, sr)
ind = torch.arange(n_taps) - (n_taps - 1) / 2
lowpass = 2 * cutoff / sr * sinc(2 * cutoff / sr * ind) * window
return lowpass
def filter_parameters(
n_layer,
n_critical,
sr_max,
cutoff_0,
cutoff_n,
stopband_0,
stopband_n
):
cutoffs = []
stopbands = []
srs = []
band_halfs = []
for i in range(n_layer):
f_c = cutoff_0 * (cutoff_n / cutoff_0) ** min(i / (n_layer - n_critical), 1)
f_t = stopband_0 * (stopband_n / stopband_0) ** min(
i / (n_layer - n_critical), 1
)
s_i = 2 ** math.ceil(math.log(min(2 * f_t, sr_max), 2))
f_h = max(f_t, s_i / 2) - f_c
cutoffs.append(f_c)
stopbands.append(f_t)
srs.append(s_i)
band_halfs.append(f_h)
return {
"cutoffs": cutoffs,
"stopbands": stopbands,
"srs": srs,
"band_halfs": band_halfs,
}
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