# Binaural Speech Enhancement Using Deep Complex Convolutional Transformer Networks

arXiv: https://arxiv.org/abs/2403.05393 · HTML: https://mlsanity.com/paper/2403.05393/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2024.

## Built on by (most cited first, 5 of 5)

- [A Survey of Deep Learning for Complex Speech Spectrograms](https://mlsanity.com/paper/2505.08694/) (2025)
- [Multichannel-to-Multichannel Target Sound Extraction Using Direction and Timestamp Clues](https://mlsanity.com/paper/2409.12415/) (2024)
- [A Lightweight and Real-Time Binaural Speech Enhancement Model with Spatial Cues Preservation](https://mlsanity.com/paper/2409.12444/) (2024)
- [Hybrid Real- and Complex-valued Neural Network Architecture](https://mlsanity.com/paper/2504.03497/) (2025)
- [BinauralVAE: Spatial Audio Reconstruction For World Models](https://mlsanity.com/paper/2609.06837/) (2026)

## Abstract (arXiv)

Studies have shown that in noisy acoustic environments, providing binaural signals to the user of an assistive listening device may improve speech intelligibility and spatial awareness. This paper presents a binaural speech enhancement method using a complex convolutional neural network with an encoder-decoder architecture and a complex multi-head attention transformer. The model is trained to estimate individual complex ratio masks in the time-frequency domain for the left and right-ear channels of binaural hearing devices. The model is trained using a novel loss function that incorporates the preservation of spatial information along with speech intelligibility improvement and noise reduction. Simulation results for acoustic scenarios with a single target speaker and isotropic noise of various types show that the proposed method improves the estimated binaural speech intelligibility and preserves the binaural cues better in comparison with several baseline algorithms.