def forward(self, est_mask):
"""
Args:
mixture_w: [M, N, K]
est_mask: [M, C, N, K]
Returns:
est_source: [M, C, T]
"""
est_source = torch.transpose(est_mask, 2, 3)
est_source = nn.AvgPool2d((1, self.L))(est_source)
est_source = overlap_and_add(est_source, self.L // 2) # M x C x T
return est_source
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [M, N, K]
est_mask: [M, C, N, K]
Returns:
est_source: [M, C, T]
"""
# D = W * M
source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [M, C, N, K]
source_w = torch.transpose(source_w, 2, 3) # [M, C, K, N]
# S = DV
est_source = self.basis_signals(source_w) # [M, C, K, L]
est_source = overlap_and_add(est_source, self.L//2) # M x C x T
return est_source
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [M, K, N]
est_mask: [M, K, C, N]
Returns:
est_source: [M, C, T]
"""
# D = W * M
source_w = torch.unsqueeze(mixture_w, 2) * est_mask # M x K x C x N
# S = DV
est_source = self.basis_signals(source_w) # M x K x C x L
est_source = est_source.permute(
(0, 2, 1, 3)).contiguous() # M x C x K x L
est_source = overlap_and_add(est_source, self.L // 2) # M x C x T
return est_source
def forward(self, mixture_w, est_mask):
"""
Args:
mixture_w: [B, E, L]
est_mask: [B, C, E, L]
Returns:
est_source: [B, C, T]
"""
# D = W * M
#print(mixture_w.shape)
#print(est_mask.shape)
source_w = torch.unsqueeze(mixture_w, 1) * est_mask # [B, C, E, L]
source_w = torch.transpose(source_w, 2, 3) # [B, C, L, E]
# S = DV
est_source = self.basis_signals(source_w) # [B, C, L, W]
est_source = overlap_and_add(est_source, self.W // 2) # B x C x T
return est_source