# Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement

arXiv: https://arxiv.org/abs/2608.07423 · HTML: https://mlsanity.com/paper/2608.07423/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 22 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Testing Framework, and Challenge Results](https://mlsanity.com/paper/2005.13981/) (2020)
- [TF-GridNet: Integrating Full- and Sub-Band Modeling for Speech Separation](https://mlsanity.com/paper/2211.12433/) (2022)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (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)
- [Toward Universal Speech Enhancement for Diverse Input Conditions](https://mlsanity.com/paper/2309.17384/) (2023)
- [Multi-Microphone Complex Spectral Mapping for Speech Dereverberation](https://mlsanity.com/paper/2003.01861/) (2020)
- [Low bit rate binaural link for improved ultra low-latency low-complexity multichannel speech enhancement in Hearing Aids](https://mlsanity.com/paper/2307.08858/) (2023)
- [Knowledge boosting during low-latency inference](https://mlsanity.com/paper/2407.11055/) (2024)
- [Modulating State Space Model with SlowFast Framework for Compute-Efficient Ultra Low-Latency Speech Enhancement](https://mlsanity.com/paper/2411.02019/) (2024)
- [Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network](https://mlsanity.com/paper/2110.11844/) (2021)
- [UX-NET: Filter-and-Process-based Improved U-Net for Real-time Time-domain Audio Separation](https://mlsanity.com/paper/2210.15822/) (2022)
- [A Time-domain Real-valued Generalized Wiener Filter for Multi-channel Neural Separation Systems](https://mlsanity.com/paper/2112.03533/) (2021)
- [On the Importance of Neural Wiener Filter for Resource Efficient Multichannel Speech Enhancement](https://mlsanity.com/paper/2401.07882/) (2024)
- [Improving Design of Input Condition Invariant Speech Enhancement](https://mlsanity.com/paper/2401.14271/) (2024)
- [Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network](https://mlsanity.com/paper/2507.13863/) (2025)

## Abstract (arXiv)

Low-latency, low-compute speech enhancement is essential for wearable devices with real-time communication requirements, but strict computational constraints significantly limit on-device performance. Knowledge Boosting has been proposed as an effective approach to improve edge model performance by leveraging a more capable server-side model, but performance gains for speech enhancement have been limited. We propose a collaborative framework incorporating three techniques: (1) delayed server output as additional input, (2) layerwise feature boosting that transfers intermediate server representations to guide edge inference, and (3) collaborative multichannel Wiener filtering, which fuses weighted covariance matrices estimated from both server and edge models for improved beamforming. Experimental results demonstrate that the proposed collaborative framework significantly outperforms the edge-only baseline with minimal additional computational overhead.