# A Lightweight Fourier-based Network for Binaural Speech Enhancement with Spatial Cue Preservation

arXiv: https://arxiv.org/abs/2509.14076 · HTML: https://mlsanity.com/paper/2509.14076/

## Key facts

- Cited by 1 papers in the map (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 26 references that are in the map.
- First posted on arXiv 17 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)

- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Deep Complex Networks](https://mlsanity.com/paper/1705.09792/) (2017)
- [Real-time binaural speech separation with preserved spatial cues](https://mlsanity.com/paper/2002.06637/) (2020)
- [Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks](https://mlsanity.com/paper/2403.05393/) (2024)
- [Binaural Speech Enhancement Using STOI-Optimal Masks](https://mlsanity.com/paper/2209.15472/) (2022)
- [Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks](https://mlsanity.com/paper/2507.20023/) (2025)
- [A Robust Target Linearly Constrained Minimum Variance Beamformer With Spatial Cues Preservation for Binaural Hearing Aids](https://mlsanity.com/paper/1811.01133/) (2018)
- [A Lightweight and Real-Time Binaural Speech Enhancement Model with Spatial Cues Preservation](https://mlsanity.com/paper/2409.12444/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Novel Binaural Cue Preservation Loss for DNN-Based Binaural Speech Enhancement](https://mlsanity.com/paper/2608.16299/) (2026)

## Abstract (arXiv)

Binaural speech enhancement faces a severe trade-off challenge, where state-of-the-art performance is achieved by computationally intensive architectures, while lightweight solutions often come at the cost of significant performance degradation. To bridge this gap, we propose the Global Adaptive Fourier Network (GAF-Net), a lightweight deep complex network that aims to establish a balance between performance and computational efficiency. The GAF-Net architecture consists of three components. First, a dual-feature encoder combining short-time Fourier transform and gammatone features enhances the robustness of acoustic representation. Second, a channel-independent globally adaptive Fourier modulator efficiently captures long-term temporal dependencies while preserving the spatial cues. Finally, a dynamic gating mechanism is implemented to reduce processing artifacts. Experimental results show that GAF-Net achieves competitive performance, particularly in terms of binaural cues (ILD and IPD error) and objective intelligibility (MBSTOI), with fewer parameters and computational cost. These results confirm that GAF-Net provides a feasible way to achieve high-fidelity binaural processing on resource-constrained devices.