asteroid.masknn.convolutional module

class asteroid.masknn.convolutional.Conv1DBlock(in_chan, hid_chan, skip_out_chan, kernel_size, padding, dilation, norm_type='gLN')

Bases: sphinx.ext.autodoc.importer._MockObject

One dimensional convolutional block, as proposed in [1].

Parameters:

• in_chan (int) – Number of input channels.
• hid_chan (int) – Number of hidden channels in the depth-wise convolution.
• skip_out_chan (int) – Number of channels in the skip convolution. If 0 or None, Conv1DBlock won’t have any skip connections. Corresponds to the the block in v1 or the paper. The forward return res instead of [res, skip] in this case.
• kernel_size (int) – Size of the depth-wise convolutional kernel.
• padding (int) – Padding of the depth-wise convolution.
• dilation (int) – Dilation of the depth-wise convolution.
• norm_type (str, optional) –

  Type of normalization to use. To choose from

  • 'gLN': global Layernorm
  • 'cLN': channelwise Layernorm
  • 'cgLN': cumulative global Layernorm

References

[1] : “Conv-TasNet: Surpassing ideal time-frequency magnitude masking for speech separation” TASLP 2019 Yi Luo, Nima Mesgarani https://arxiv.org/abs/1809.07454

forward(x)

Input shape [batch, feats, seq]

class asteroid.masknn.convolutional.SuDORMRF(in_chan, n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='softmax')

Bases: sphinx.ext.autodoc.importer._MockObject

SuDORMRF mask network, as described in [1].

Parameters:

• in_chan (int) – Number of input channels. Also number of output channels.
• n_src (int) – Number of sources in the input mixtures.
• bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.
• num_blocks (int) – Number of of UBlocks.
• upsampling_depth (int) – Depth of upsampling.
• mask_act (str) – Name of output activation.

References

[1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”,

Tzinis et al. MLSP 2020.

forward(x)

get_config()

class asteroid.masknn.convolutional.SuDORMRFImproved(in_chan, n_src, bn_chan=128, num_blocks=16, upsampling_depth=4, mask_act='relu')

Bases: sphinx.ext.autodoc.importer._MockObject

Improved SuDORMRF mask network, as described in [1].

Parameters:

• in_chan (int) – Number of input channels. Also number of output channels.
• n_src (int) – Number of sources in the input mixtures.
• bn_chan (int, optional) – Number of bins in the bottleneck layer and the UNet blocks.
• num_blocks (int) – Number of of UBlocks
• upsampling_depth (int) – Depth of upsampling
• mask_act (str) – Name of output activation.

References

[1] : “Sudo rm -rf: Efficient Networks for Universal Audio Source Separation”,

Tzinis et al. MLSP 2020.

forward(x)

get_config()

class asteroid.masknn.convolutional.TDConvNet(in_chan, n_src, out_chan=None, n_blocks=8, n_repeats=3, bn_chan=128, hid_chan=512, skip_chan=128, conv_kernel_size=3, norm_type='gLN', mask_act='relu', kernel_size=None)

Bases: sphinx.ext.autodoc.importer._MockObject

Temporal Convolutional network used in ConvTasnet.

Parameters:

• in_chan (int) – Number of input filters.
• n_src (int) – Number of masks to estimate.
• out_chan (int, optional) – Number of bins in the estimated masks. If None, out_chan = in_chan.
• n_blocks (int, optional) – Number of convolutional blocks in each repeat. Defaults to 8.
• n_repeats (int, optional) – Number of repeats. Defaults to 3.
• bn_chan (int, optional) – Number of channels after the bottleneck.
• hid_chan (int, optional) – Number of channels in the convolutional blocks.
• skip_chan (int, optional) – Number of channels in the skip connections. If 0 or None, TDConvNet won’t have any skip connections and the masks will be computed from the residual output. Corresponds to the ConvTasnet architecture in v1 or the paper.
• conv_kernel_size (int, optional) – Kernel size in convolutional blocks.
• norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.
• mask_act (str, optional) – Which non-linear function to generate mask.

References

[1] : “Conv-TasNet: Surpassing ideal time-frequency magnitude masking for speech separation” TASLP 2019 Yi Luo, Nima Mesgarani https://arxiv.org/abs/1809.07454

forward(mixture_w)

Parameters:mixture_w (torch.Tensor) – Tensor of shape [batch, n_filters, n_frames] Returns:torch.Tensor – estimated mask of shape [batch, n_src, n_filters, n_frames]

get_config()

class asteroid.masknn.convolutional.TDConvNetpp(in_chan, n_src, out_chan=None, n_blocks=8, n_repeats=3, bn_chan=128, hid_chan=512, skip_chan=128, conv_kernel_size=3, norm_type='fgLN', mask_act='relu')

Bases: sphinx.ext.autodoc.importer._MockObject

Improved Temporal Convolutional network used in [1] (TDCN++)

Parameters:

• in_chan (int) – Number of input filters.
• n_src (int) – Number of masks to estimate.
• out_chan (int, optional) – Number of bins in the estimated masks. If None, out_chan = in_chan.
• n_blocks (int, optional) – Number of convolutional blocks in each repeat. Defaults to 8.
• n_repeats (int, optional) – Number of repeats. Defaults to 3.
• bn_chan (int, optional) – Number of channels after the bottleneck.
• hid_chan (int, optional) – Number of channels in the convolutional blocks.
• skip_chan (int, optional) – Number of channels in the skip connections. If 0 or None, TDConvNet won’t have any skip connections and the masks will be computed from the residual output. Corresponds to the ConvTasnet architecture in v1 or the paper.
• kernel_size (int, optional) – Kernel size in convolutional blocks.
• norm_type (str, optional) – To choose from 'BN', 'gLN', 'cLN'.
• mask_act (str, optional) – Which non-linear function to generate mask.

References

[1] : Kavalerov, Ilya et al. “Universal Sound Separation.” in WASPAA 2019

Notes

The differences wrt to ConvTasnet’s TCN are 1. Channel wise layer norm instead of global 2. Longer-range skip-residual connections from earlier repeat inputs

to later repeat inputs after passing them through dense layer.

3. Learnable scaling parameter after each dense layer. The scaling

   parameter for the second dense layer in each convolutional block (which is applied rightbefore the residual connection) is initialized to an exponentially decaying scalar equal to 0.9**L, where L is the layer or block index.

forward(mixture_w)

Parameters:mixture_w (torch.Tensor) – Tensor of shape [batch, n_filters, n_frames] Returns:torch.Tensor – estimated mask of shape [batch, n_src, n_filters, n_frames]

get_config()

class asteroid.masknn.convolutional.UBlock(out_chan=128, in_chan=512, upsampling_depth=4)

Bases: asteroid.masknn.convolutional._BaseUBlock

Upsampling block.

Based on the following principle:

REDUCE ---> SPLIT ---> TRANSFORM --> MERGE

forward(x)

Parameters:x – input feature map Returns:transformed feature map

class asteroid.masknn.convolutional.UConvBlock(out_chan=128, in_chan=512, upsampling_depth=4)

Bases: asteroid.masknn.convolutional._BaseUBlock

Block which performs successive downsampling and upsampling in order to be able to analyze the input features in multiple resolutions.

forward(x)

Args

x: input feature map

Returns:transformed feature map