# HyBeam: Hybrid Microphone-Beamforming Array-Agnostic Speech Enhancement for Wearables

arXiv: https://arxiv.org/abs/2510.22637 · HTML: https://mlsanity.com/paper/2510.22637/

## 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 14 of its 24 references that are in the map.
- First posted on arXiv 26 Oct 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, 14 of 14)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [End-to-end Microphone Permutation and Number Invariant Multi-channel Speech Separation](https://mlsanity.com/paper/1910.14104/) (2019)
- [Channel-Attention Dense U-Net for Multichannel Speech Enhancement](https://mlsanity.com/paper/2001.11542/) (2020)
- [Insights Into Deep Non-linear Filters for Improved Multi-channel Speech Enhancement](https://mlsanity.com/paper/2206.13310/) (2022)
- [Multi-Microphone Complex Spectral Mapping for Speech Dereverberation](https://mlsanity.com/paper/2003.01861/) (2020)
- [VarArray: Array-Geometry-Agnostic Continuous Speech Separation](https://mlsanity.com/paper/2110.05745/) (2021)
- [One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement](https://mlsanity.com/paper/2110.10330/) (2021)
- [Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement](https://mlsanity.com/paper/2107.12601/) (2021)
- [AGADIR: Towards Array-Geometry Agnostic Directional Speech Recognition](https://mlsanity.com/paper/2401.10411/) (2024)
- [Time-domain Ad-hoc Array Speech Enhancement Using a Triple-path Network](https://mlsanity.com/paper/2110.11844/) (2021)
- [Attention-Driven Multichannel Speech Enhancement in Moving Sound Source Scenarios](https://mlsanity.com/paper/2312.10756/) (2023)
- [Guided Speech Enhancement Network](https://mlsanity.com/paper/2303.07486/) (2023)
- [Array Configuration-Agnostic Personalized Speech Enhancement using Long-Short-Term Spatial Coherence](https://mlsanity.com/paper/2211.08748/) (2022)

## Abstract (arXiv)

Speech enhancement is a fundamental challenge in signal processing, particularly when robustness is required across diverse acoustic conditions and microphone setups. Deep learning methods have been successful for speech enhancement, but often assume fixed array geometries, limiting their use in mobile, embedded, and wearable devices. Existing array-agnostic approaches typically rely on either raw microphone signals or beamformer outputs, but both have drawbacks under changing geometries. We introduce HyBeam, a hybrid framework that uses raw microphone signals at low frequencies and beamformer signals at higher frequencies, exploiting their complementary strengths while remaining highly array-agnostic. Simulations across diverse rooms and wearable array configurations demonstrate that HyBeam consistently surpasses microphone-only and beamformer-only baselines in PESQ, STOI, and SI-SDR. A bandwise analysis shows that the hybrid approach leverages beamformer directivity at high frequencies and microphone cues at low frequencies, outperforming either method alone across all bands.