# An Analysis of Joint Nonlinear Spatial Filtering for Spatial Aliasing Reduction

arXiv: https://arxiv.org/abs/2509.25982 · HTML: https://mlsanity.com/paper/2509.25982/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 8 of its 27 references that are in the map.
- First posted on arXiv 30 Sep 2025.

## Placed in

- Speech and audio language models / [Speech enhancement and noise suppression](https://mlsanity.com/speech/speech-enhancement-and-noise-suppression/)

## Builds on (most cited first, 8 of 8)

- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation](https://mlsanity.com/paper/2307.16516/) (2023)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Multi-channel Speech Separation Using Spatially Selective Deep Non-linear Filters](https://mlsanity.com/paper/2304.12023/) (2023)
- [Nonlinear Spatial Filtering in Multichannel Speech Enhancement](https://mlsanity.com/paper/2104.11033/) (2021)
- [Spatially Selective Deep Non-linear Filters for Speaker Extraction](https://mlsanity.com/paper/2211.02420/) (2022)
- [Explainable DNN-based Beamformer with Postfilter](https://mlsanity.com/paper/2411.10854/) (2024)
- [Deep learning based spatial aliasing reduction in beamforming for audio capture](https://mlsanity.com/paper/2505.19781/) (2025)

## Abstract (arXiv)

The performance of traditional linear spatial filters for speech enhancement is constrained by the physical size and number of channels of microphone arrays. For instance, for large microphone distances and high frequencies, spatial aliasing may occur, leading to unwanted enhancement of signals from non-target directions. Recently, it has been proposed to replace linear beamformers by nonlinear deep neural networks for joint spatial-spectral processing. While it has been shown that such approaches result in higher performance in terms of instrumental quality metrics, in this work we highlight their ability to efficiently handle spatial aliasing. In particular, we show that joint spatial and tempo-spectral processing is more robust to spatial aliasing than traditional approaches that perform spatial processing alone or separately with tempo-spectral filtering. The results provide another strong motivation for using deep nonlinear networks in multichannel speech enhancement, beyond their known benefits in managing non-Gaussian noise and multiple speakers, especially when microphone arrays with rather large microphone distances are used.