# Implicit Neural Spatial Filtering for Multichannel Source Separation in the Waveform Domain

arXiv: https://arxiv.org/abs/2206.15423 · HTML: https://mlsanity.com/paper/2206.15423/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 26 of 26)

- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [Supervised Speech Separation Based on Deep Learning: An Overview](https://mlsanity.com/paper/1708.07524/) (2017)
- [Real Time Speech Enhancement in the Waveform Domain](https://mlsanity.com/paper/2006.12847/) (2020)
- [Music Source Separation in the Waveform Domain](https://mlsanity.com/paper/1911.13254/) (2019)
- [Hybrid Spectrogram and Waveform Source Separation](https://mlsanity.com/paper/2111.03600/) (2021)
- [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)
- [The Cone of Silence: Speech Separation by Localization](https://mlsanity.com/paper/2010.06007/) (2020)
- [ADL-MVDR: All deep learning MVDR beamformer for target speech separation](https://mlsanity.com/paper/2008.06994/) (2020)
- [Channel-Attention Dense U-Net for Multichannel Speech Enhancement](https://mlsanity.com/paper/2001.11542/) (2020)
- [Enhancing End-to-End Multi-channel Speech Separation via Spatial Feature Learning](https://mlsanity.com/paper/2003.03927/) (2020)
- [End-to-End Multi-Channel Speech Separation](https://mlsanity.com/paper/1905.06286/) (2019)
- [Neural Spatio-Temporal Beamformer for Target Speech Separation](https://mlsanity.com/paper/2005.03889/) (2020)
- [End-to-End Far-Field Speech Recognition with Unified Dereverberation and Beamforming](https://mlsanity.com/paper/2005.10479/) (2020)
- [Multi-microphone Complex Spectral Mapping for Utterance-wise and Continuous Speech Separation](https://mlsanity.com/paper/2010.01703/) (2020)
- [Multi-Microphone Complex Spectral Mapping for Speech Dereverberation](https://mlsanity.com/paper/2003.01861/) (2020)
- [DESNet: A Multi-channel Network for Simultaneous Speech Dereverberation, Enhancement and Separation](https://mlsanity.com/paper/2011.02131/) (2020)
- [On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments](https://mlsanity.com/paper/2011.05958/) (2020)
- [An End-to-end Architecture of Online Multi-channel Speech Separation](https://mlsanity.com/paper/2009.03141/) (2020)
- [Multi-Channel Speech Enhancement using Graph Neural Networks](https://mlsanity.com/paper/2102.06934/) (2021)
- [Deep Learning Based Speech Beamforming](https://mlsanity.com/paper/1802.05383/) (2018)
- [Multichannel Speech Enhancement by Raw Waveform-mapping using Fully Convolutional Networks](https://mlsanity.com/paper/1909.11909/) (2019)
- [Cracking the cocktail party problem by multi-beam deep attractor network](https://mlsanity.com/paper/1803.10924/) (2018)
- [Complex Neural Spatial Filter: Enhancing Multi-channel Target Speech Separation in Complex Domain](https://mlsanity.com/paper/2104.12359/) (2021)
- [Multichannel Speech Enhancement without Beamforming](https://mlsanity.com/paper/2110.13130/) (2021)
- [A Neural Beam Filter for Real-time Multi-channel Speech Enhancement](https://mlsanity.com/paper/2202.02500/) (2022)

## Built on by (most cited first, 6 of 6)

- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [ReZero: Region-customizable Sound Extraction](https://mlsanity.com/paper/2308.16892/) (2023)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [Multi-Microphone Speaker Separation by Spatial Regions](https://mlsanity.com/paper/2303.07143/) (2023)
- [Attention-Driven Multichannel Speech Enhancement in Moving Sound Source Scenarios](https://mlsanity.com/paper/2312.10756/) (2023)
- [End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer Functions](https://mlsanity.com/paper/2502.06285/) (2025)

## Abstract (arXiv)

We present a single-stage casual waveform-to-waveform multichannel model that can separate moving sound sources based on their broad spatial locations in a dynamic acoustic scene. We divide the scene into two spatial regions containing, respectively, the target and the interfering sound sources. The model is trained end-to-end and performs spatial processing implicitly, without any components based on traditional processing or use of hand-crafted spatial features. We evaluate the proposed model on a real-world dataset and show that the model matches the performance of an oracle beamformer followed by a state-of-the-art single-channel enhancement network.