asteroid.models.conv_tasnet module¶

class asteroid.models.conv_tasnet.ConvTasNet(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='sigmoid', in_chan=None, fb_name='free', kernel_size=16, n_filters=512, stride=8, encoder_activation=None, **fb_kwargs)[source]¶

Bases: asteroid.models.base_models.BaseEncoderMaskerDecoder

ConvTasNet separation model, as described in [1].

Parameters:

n_src (int) – Number of sources in the input mixtures.
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.
in_chan (int, optional) – Number of input channels, should be equal to n_filters.
fb_name (str, className) – Filterbank family from which to make encoder and decoder. To choose among ['free', 'analytic_free', 'param_sinc', 'stft'].
n_filters (int) – Number of filters / Input dimension of the masker net.
kernel_size (int) – Length of the filters.
stride (int, optional) – Stride of the convolution. If None (default), set to kernel_size // 2.
**fb_kwargs (dict) – Additional kwards to pass to the filterbank creation.

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