| import numpy as np |
| import torch |
| import torch.nn.functional as F |
| from scipy import signal as sig |
|
|
|
|
| |
| |
| class PQMF(torch.nn.Module): |
| def __init__(self, N=4, taps=62, cutoff=0.15, beta=9.0): |
| super().__init__() |
|
|
| self.N = N |
| self.taps = taps |
| self.cutoff = cutoff |
| self.beta = beta |
|
|
| QMF = sig.firwin(taps + 1, cutoff, window=("kaiser", beta)) |
| H = np.zeros((N, len(QMF))) |
| G = np.zeros((N, len(QMF))) |
| for k in range(N): |
| constant_factor = ( |
| (2 * k + 1) * (np.pi / (2 * N)) * (np.arange(taps + 1) - ((taps - 1) / 2)) |
| ) |
| phase = (-1) ** k * np.pi / 4 |
| H[k] = 2 * QMF * np.cos(constant_factor + phase) |
|
|
| G[k] = 2 * QMF * np.cos(constant_factor - phase) |
|
|
| H = torch.from_numpy(H[:, None, :]).float() |
| G = torch.from_numpy(G[None, :, :]).float() |
|
|
| self.register_buffer("H", H) |
| self.register_buffer("G", G) |
|
|
| updown_filter = torch.zeros((N, N, N)).float() |
| for k in range(N): |
| updown_filter[k, k, 0] = 1.0 |
| self.register_buffer("updown_filter", updown_filter) |
| self.N = N |
|
|
| self.pad_fn = torch.nn.ConstantPad1d(taps // 2, 0.0) |
|
|
| def forward(self, x): |
| return self.analysis(x) |
|
|
| def analysis(self, x): |
| return F.conv1d(x, self.H, padding=self.taps // 2, stride=self.N) |
|
|
| def synthesis(self, x): |
| x = F.conv_transpose1d(x, self.updown_filter * self.N, stride=self.N) |
| x = F.conv1d(x, self.G, padding=self.taps // 2) |
| return x |
|
|
