The network architecture related to dual-path RNN TasNet. about tac HOT 3 CLOSED

Yes it is something like that.

from tac.

No it's not completely the same as the problem definition of TasNet and FaSNet are different: TasNet attempts to estimate multiplicative masks on the encoder outputs, while FaSNet attempts to estimate the time-domain beamforming filter coefficients. The gated output layer is only applied for FaSNet to ensure the dynamic range of the estimated beamforming filters is between -1 and 1, but empirically removing the gating layer or even using a linear output layer should have a similar (or same) performance.

In DPRNN-TasNet the output layer is simply self.output without the gating layer, and the nonlinearity for the output layer can be either Sigmoid or ReLU (empirically ReLU might be better).

from tac.

Thanks! I will read the FaSNet paper.
So, the network architecture of DPRNN-TasNet is like

class DPRNNTasNet(...):
    def __init__(...):
        ...
        self.encoder = nn.Conv1d(...)
        self.enc_LN = nn.GroupNorm(...) # Layer normalization
        self.BN = nn.Conv1d(...) # Bottleneck convolution.
        self.DPRNN = DPRNN(...) # includes nn.PReLU() and nn.Conv2d(...) for mask estimation
        self.output = nn.Sequential(nn.Conv1d(...), nn.ReLU()) # empirically better
        # or self.output = nn.Sequential(nn.Conv1d(...), nn.Sigmoid())
        self.decoder = nn.ConvTranspose1d(...)

Do I understand your explanation correctly?

from tac.