# SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation

arXiv: https://arxiv.org/abs/2307.16516 · HTML: https://mlsanity.com/paper/2307.16516/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2023.

## Built on by (most cited first, 17 of 17)

- [ArrayDPS: Unsupervised Blind Speech Separation with a Diffusion Prior](https://mlsanity.com/paper/2505.05657/) (2025)
- [DeFT-Mamba: Universal Multichannel Sound Separation and Polyphonic Audio Classification](https://mlsanity.com/paper/2409.12413/) (2024)
- [A Survey of Deep Learning for Complex Speech Spectrograms](https://mlsanity.com/paper/2505.08694/) (2025)
- [Schr"odinger Bridge Mamba for One-Step Speech Enhancement](https://mlsanity.com/paper/2510.16834/) (2025)
- [CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel Pruning](https://mlsanity.com/paper/2410.11062/) (2024)
- [LMFCA-Net: A Lightweight Model for Multi-Channel Speech Enhancement with Efficient Narrow-Band and Cross-Band Attention](https://mlsanity.com/paper/2502.11462/) (2025)
- [CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASR](https://mlsanity.com/paper/2502.20040/) (2025)
- [Adaptive Rotary Steering with Joint Autoregression for Robust Extraction of Closely Moving Speakers in Dynamic Scenarios](https://mlsanity.com/paper/2601.12345/) (2026)
- [AV-CrossNet: an Audiovisual Complex Spectral Mapping Network for Speech Separation By Leveraging Narrow- and Cross-Band Modeling](https://mlsanity.com/paper/2406.11619/) (2024)
- [Autoregressive Guidance of Deep Spatially Selective Filters using Bayesian Tracking for Efficient Extraction of Moving Speakers](https://mlsanity.com/paper/2603.23723/) (2026)
- [Improving multichannel speech enhancement through accurate room-acoustic simulations](https://mlsanity.com/paper/2606.31552/) (2026)
- [Mask-Weighted Spatial Likelihood Coding for Speaker-Independent Joint Localization and Mask Estimation](https://mlsanity.com/paper/2410.19595/) (2024)
- [LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation](https://mlsanity.com/paper/2510.10687/) (2025)
- [AMDM-SE: Attention-based Multichannel Diffusion Model for Speech Enhancement](https://mlsanity.com/paper/2601.13140/) (2026)
- [MeCo: One-Step MeanFlow-based Corrector for Multi-Channel Speech Separation](https://mlsanity.com/paper/2606.09677/) (2026)
- [Weakly Guided and Autoregressive Beamformer Parameterization for Generalizable Moving Speaker Extraction in Higher-Order Ambisonics](https://mlsanity.com/paper/2607.04471/) (2026)
- [Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement](https://mlsanity.com/paper/2608.07423/) (2026)

## Abstract (arXiv)

This work proposes a neural network to extensively exploit spatial information for multichannel joint speech separation, denoising and dereverberation, named SpatialNet. In the short-time Fourier transform (STFT) domain, the proposed network performs end-to-end speech enhancement. It is mainly composed of interleaved narrow-band and cross-band blocks to respectively exploit narrow-band and cross-band spatial information. The narrow-band blocks process frequencies independently, and use self-attention mechanism and temporal convolutional layers to respectively perform spatial-feature-based speaker clustering and temporal smoothing/filtering. The cross-band blocks process frames independently, and use full-band linear layer and frequency convolutional layers to respectively learn the correlation between all frequencies and adjacent frequencies. Experiments are conducted on various simulated and real datasets, and the results show that 1) the proposed network achieves the state-of-the-art performance on almost all tasks; 2) the proposed network suffers little from the spectral generalization problem; and 3) the proposed network is indeed performing speaker clustering (demonstrated by attention maps).