# ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids

arXiv: https://arxiv.org/abs/2609.00966 · HTML: https://mlsanity.com/paper/2609.00966/

## 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 21 of its 59 references that are in the map.
- First posted on arXiv 1 Sep 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, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [RepVGG: Making VGG-style ConvNets Great Again](https://mlsanity.com/paper/2101.03697/) (2021)
- [FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement](https://mlsanity.com/paper/2010.15508/) (2020)
- [SpatialNet: Extensively Learning Spatial Information for Multichannel Joint Speech Separation, Denoising and Dereverberation](https://mlsanity.com/paper/2307.16516/) (2023)
- [Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks](https://mlsanity.com/paper/2403.05393/) (2024)
- [Microphone Array Signal Processing and Deep Learning for Speech Enhancement](https://mlsanity.com/paper/2501.07215/) (2025)
- [Spatially Selective Active Noise Control Systems](https://mlsanity.com/paper/2208.09997/) (2022)
- [Binaural Speech Enhancement Using STOI-Optimal Masks](https://mlsanity.com/paper/2209.15472/) (2022)
- [Real-time multichannel deep speech enhancement in hearing aids: Comparing monaural and binaural processing in complex acoustic scenarios](https://mlsanity.com/paper/2405.01967/) (2024)
- [Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks](https://mlsanity.com/paper/2507.20023/) (2025)
- [A Lightweight and Real-Time Binaural Speech Enhancement Model with Spatial Cues Preservation](https://mlsanity.com/paper/2409.12444/) (2024)
- [RTF-steered binaural MVDR beamforming incorporating multiple external microphones](https://mlsanity.com/paper/1908.04848/) (2019)
- [Modeling the Comb Filter Effect and Interaural Coherence for Binaural Source Separation](https://mlsanity.com/paper/1910.02127/) (2019)
- [Time-Variance Aware Real-Time Speech Enhancement](https://mlsanity.com/paper/2302.13063/) (2023)
- [Effect of target signals and delays on spatially selective active noise control for open-fitting hearables](https://mlsanity.com/paper/2401.07681/) (2024)
- [Active Speech Enhancement: Active Speech Denoising Decliping and Deveraberation](https://mlsanity.com/paper/2505.16911/) (2025)

## Abstract (arXiv)

Open-fit hearing aids have attracted growing attention due to their superior wearing comfort. However, the open-fit design inevitably causes acoustic leakage into the ear canal, degrading the performance of existing binaural speech enhancement (BSE). To this end, we propose ABSE-NET, an active BSE framework integrating active noise control (ANC) with BSE to jointly enhance target speech and suppress acoustic leakage. The ABSE-NET pipeline cascades a binaural MVDR (BMVDR) with a lightweight neural network (LNN). The former achieves a coarse BSE, whereas the latter simultaneously cancels acoustic leakage and compensates for BMVDR-induced distortion. The LNN uses an encoder-decoder with a feature fusion module, which includes frequency-time dependency learning and convolutional attention blocks. Unlike traditional BSE+ANC solutions via adaptive filtering, ABSE-NET needs no in-ear microphone in practical deployment. Experiments validate its superiority over state-of-the-art methods. Code repository: https://github.com/Bream101/ABSE-NET.