Real-time Single-channel Dereverberation and Separation with Time-domain Audio Separation Network
By Yi Luo, Nima Mesgarani. 2018
78 citations recorded by the source corpus.
We investigate the recently proposed Time-domain Audio Separation Network (TasNet) in the task of real-time single-channel speech dereverberation. Unlike systems that take time-frequency representation of the audio as input, TasNet learns an adaptive front-end in replacement of the time-frequency representation by a time-domain convolutional non-negative au-toencoder. We show that by formulating the dereverberation problem as a denoising problem where the direct path is separated from the reverberations, a TasNet denoising autoencoder can outperform a deep LSTM baseline on log-power magnitude spectrogram input in both causal and non-causal settings. We further show that adjusting the stride size in the convolutional autoencoder helps both the dereverberation and separation performance.
Subfields: Signal Processing
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In Signal Processing
- Zhuo Chen
- Shinji Watanabe
- Jonathan Le Roux
- Yi Luo
- Takuya Yoshioka
- Jinyu Li
- John R. Hershey
- Gordon Wichern
Citations in the corpus
- Dual-Path RNN: Efficient Long Sequence Modeling for Time-Domain Single-Channel Speech Separation (2019, methodology): “Although DPRNN can be applied to any systems that require longterm sequential modeling, we investigate its application to the timedomain audio separation network (TasNet) [1, 4, 22], an adaptive front-end method that achieves high speech separation performance on a benchmarking dataset.”
- WHAMR!: Noisy and Reverberant Single-Channel Speech Separation (2019, methodology): “The newer network uses the same configuration, but is trained with more aggressive gradient clipping and stagnation learning rate adjustment, which supports the findings regarding training optimizer parameters reported in [5, 17].”
- Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals (2020, background): “Speech Separation As the core part of the cocktail party problem [7], speech separation draws much attention recently [13, 15, 51, 19, 23, 20, 21, 24].”