# Spatial-Magnifier: Spatial upsampling for multichannel speech enhancement

arXiv: https://arxiv.org/abs/2605.04749 · HTML: https://mlsanity.com/paper/2605.04749/

## 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 19 of its 34 references that are in the map.
- First posted on arXiv 6 May 2026.

## 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, 19 of 19)

- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [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)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [Deep Back-Projection Networks For Super-Resolution](https://mlsanity.com/paper/1803.02735/) (2018)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement](https://mlsanity.com/paper/2209.11112/) (2022)
- [SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation](https://mlsanity.com/paper/2307.16516/) (2023)
- [STFT-Domain Neural Speech Enhancement with Very Low Algorithmic Latency](https://mlsanity.com/paper/2204.09911/) (2022)
- [Multi-Microphone Complex Spectral Mapping for Speech Dereverberation](https://mlsanity.com/paper/2003.01861/) (2020)
- [DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing](https://mlsanity.com/paper/2308.15777/) (2023)
- [Robust DOA estimation using deep acoustic imaging](https://mlsanity.com/paper/2401.08717/) (2024)
- [Self-Supervised Learning of Spatial Acoustic Representation with Cross-Channel Signal Reconstruction and Multi-Channel Conformer](https://mlsanity.com/paper/2312.00476/) (2023)
- [DeFT-Mamba: Universal Multichannel Sound Separation and Polyphonic Audio Classification](https://mlsanity.com/paper/2409.12413/) (2024)
- [FoVNet: Configurable Field-of-View Speech Enhancement with Low Computation and Distortion for Smart Glasses](https://mlsanity.com/paper/2408.06468/) (2024)
- [Efficient Audiovisual Speech Processing via MUTUD: Multimodal Training and Unimodal Deployment](https://mlsanity.com/paper/2501.18157/) (2025)
- [Neural Network-based Virtual Microphone Estimator](https://mlsanity.com/paper/2101.04315/) (2021)
- [Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss](https://mlsanity.com/paper/2311.11595/) (2023)

## Abstract (arXiv)

While the spatial directivity of multichannel speech enhancement algorithms improves with the number of microphones, fitting large capture arrays into real-world edge devices is typically limited by physical constraints. To overcome this limitation, we propose Spatial-Magnifier, a neural network designed to generate virtual microphone (VM) signals from a limited set of real microphone (RM) measurements. Moreover, we introduce the Spatial Audio Representation Learning (SARL) framework, which leverages estimated VM signals and features to condition a downstream speech enhancement system. Experimental results demonstrate that the proposed framework outperforms existing spatial upsampling baselines across various speech extraction systems, including end-to-end multichannel speech enhancement and neural beamforming. The proposed method nearly recovers the oracle performance achieved when all microphones are available.