asteroid.models.lstm_tasnet module¶

class asteroid.models.lstm_tasnet.LSTMTasNet(n_src, out_chan=None, rnn_type='lstm', n_layers=4, hid_size=512, dropout=0.3, mask_act='sigmoid', bidirectional=True, in_chan=None, fb_name='free', n_filters=64, kernel_size=16, stride=8, encoder_activation=None, **fb_kwargs)[source]¶

Bases: asteroid.models.base_models.BaseTasNet

TasNet separation model, as described in [1].

Parameters:

n_src (int) – Number of masks to estimate.
out_chan (int or None) – Number of bins in the estimated masks. Defaults to in_chan.
hid_size (int) – Number of neurons in the RNNs cell state. Defaults to 128.
mask_act (str, optional) – Which non-linear function to generate mask.
bidirectional (bool, optional) – True for bidirectional Inter-Chunk RNN (Intra-Chunk is always bidirectional).
rnn_type (str, optional) – Type of RNN used. Choose between 'RNN', 'LSTM' and 'GRU'.
n_layers (int, optional) – Number of layers in each RNN.
dropout (float, optional) – Dropout ratio, must be in [0,1].
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]: Yi Luo et al. “Real-time Single-channel Dereverberation and Separation: with Time-domain Audio Separation Network”, Interspeech 2018