Comments (3)
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.
Related Issues (14)
- How to use the dataset in the paper? HOT 2
- how to understand the rescaling with snr in create_dataset.py HOT 2
- Use FasNet as Frontend HOT 2
- Q about the data generation!
- What's the meaning of the ad-hoc array?
- can FasNet be used to be a frontend for subsequent AEC
- how to run this project HOT 1
- how to calculate SI-SNRi in multi-channel speech separation task HOT 1
- How to train the model? HOT 3
- how to generate the pkl? HOT 3
- num_mic array HOT 2
- Loss function HOT 3
- Could you please share the training script?
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from tac.