# A Time-domain Real-valued Generalized Wiener Filter for Multi-channel Neural Separation Systems

arXiv: https://arxiv.org/abs/2112.03533 · HTML: https://mlsanity.com/paper/2112.03533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 60 references that are in the map.
- First posted on arXiv 7 Dec 2021.

## Builds on (most cited first, 30 of 30)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation](https://mlsanity.com/paper/1607.00325/) (2016)
- [Attention is All You Need in Speech Separation](https://mlsanity.com/paper/2010.13154/) (2020)
- [Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation](https://mlsanity.com/paper/1910.06379/) (2019)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [Wavesplit: End-to-End Speech Separation by Speaker Clustering](https://mlsanity.com/paper/2002.08933/) (2020)
- [Phase-aware Speech Enhancement with Deep Complex U-Net](https://mlsanity.com/paper/1903.03107/) (2019)
- [Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation](https://mlsanity.com/paper/2007.13975/) (2020)
- [Sudo rm -rf: Efficient Networks for Universal Audio Source Separation](https://mlsanity.com/paper/2007.06833/) (2020)
- [gpuRIR: A Python Library for Room Impulse Response Simulation with GPU Acceleration](https://mlsanity.com/paper/1810.11359/) (2018)
- [Voice Separation with an Unknown Number of Multiple Speakers](https://mlsanity.com/paper/2003.01531/) (2020)
- [MIMO-SPEECH: End-to-End Multi-Channel Multi-Speaker Speech Recognition](https://mlsanity.com/paper/1910.06522/) (2019)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [FaSNet: Low-latency Adaptive Beamforming for Multi-microphone Audio Processing](https://mlsanity.com/paper/1909.13387/) (2019)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [End-to-end music source separation: is it possible in the waveform domain?](https://mlsanity.com/paper/1810.12187/) (2018)
- [FurcaNeXt: End-to-end monaural speech separation with dynamic gated dilated temporal convolutional networks](https://mlsanity.com/paper/1902.04891/) (2019)
- [Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning](https://mlsanity.com/paper/2003.03927/) (2020)
- [End-to-End Dereverberation, Beamforming, and Speech Recognition with Improved Numerical Stability and Advanced Frontend](https://mlsanity.com/paper/2102.11525/) (2021)
- [End-to-End Multi-Channel Speech Separation](https://mlsanity.com/paper/1905.06286/) (2019)
- [Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation](https://mlsanity.com/paper/2010.01703/) (2020)
- [End-to-end Source Separation with Adaptive Front-Ends](https://mlsanity.com/paper/1705.02514/) (2017)
- [Deep Learning Based Speech Beamforming](https://mlsanity.com/paper/1802.05383/) (2018)
- [Implicit Filter-and-sum Network for Multi-channel Speech Separation](https://mlsanity.com/paper/2011.08401/) (2020)
- [WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation](https://mlsanity.com/paper/2011.09162/) (2020)
- [Beam-Guided TasNet: An Iterative Speech Separation Framework with Multi-Channel Output](https://mlsanity.com/paper/2102.02998/) (2021)

## Built on by (most cited first, 1 of 1)

- [Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement](https://mlsanity.com/paper/2608.07423/) (2026)

## Abstract (arXiv)

Frequency-domain beamformers have been successful in a wide range of multi-channel neural separation systems in the past years. However, the operations in conventional frequency-domain beamformers are typically independently-defined and complex-valued, which result in two drawbacks: the former does not fully utilize the advantage of end-to-end optimization, and the latter may introduce numerical instability during the training phase. Motivated by the recent success in end-to-end neural separation systems, in this paper we propose time-domain real-valued generalized Wiener filter (TD-GWF), a linear filter defined on a 2-D learnable real-valued signal transform. TD-GWF splits the transformed representation into groups and performs an minimum mean-square error (MMSE) estimation on all available channels on each of the groups. We show how TD-GWF can be connected to conventional filter-and-sum beamformers when certain signal transform and the number of groups are specified. Moreover, given the recent success in the sequential neural beamforming frameworks, we show how TD-GWF can be applied in such frameworks to perform iterative beamforming and separation to obtain an overall performance gain. Comprehensive experiment results show that TD-GWF performs consistently better than conventional frequency-domain beamformers in the sequential neural beamforming pipeline with various neural network architectures, microphone array scenarios, and task configurations.