# Binaural Speech Enhancement Using Complex Convolutional Recurrent Networks

arXiv: https://arxiv.org/abs/2507.20023 · HTML: https://mlsanity.com/paper/2507.20023/

## Key facts

- Cited by 3 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 27 references that are in the map.
- First posted on arXiv 26 Jul 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)

- [Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation](https://mlsanity.com/paper/1809.07454/) (2018)
- [DCCRN: Deep Complex Convolution Recurrent Network for Phase-Aware Speech Enhancement](https://mlsanity.com/paper/2008.00264/) (2020)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [PHASEN: A Phase-and-Harmonics-Aware Speech Enhancement Network](https://mlsanity.com/paper/1911.04697/) (2019)
- [T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement](https://mlsanity.com/paper/1910.06762/) (2019)
- [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)

## Built on by (most cited first, 3 of 3)

- [A Lightweight Fourier-based Network for Binaural Speech Enhancement with Spatial Cue Preservation](https://mlsanity.com/paper/2509.14076/) (2025)
- [A Novel Binaural Cue Preservation Loss for DNN-Based Binaural Speech Enhancement](https://mlsanity.com/paper/2608.16299/) (2026)
- [ABSE-NET: A Lightweight Neural Model for Active Binaural Speech Enhancement in Open-Fit Hearing Aids](https://mlsanity.com/paper/2609.00966/) (2026)

## Abstract (arXiv)

From hearing aids to augmented and virtual reality devices, binaural speech enhancement algorithms have been established as state-of-the-art techniques to improve speech intelligibility and listening comfort. In this paper, we present an end-to-end binaural speech enhancement method using a complex recurrent convolutional network with an encoder-decoder architecture and a complex LSTM recurrent block placed between the encoder and decoder. A loss function that focuses on the preservation of spatial information in addition to speech intelligibility improvement and noise reduction is introduced. The network estimates individual complex ratio masks for the left and right-ear channels of a binaural hearing device in the time-frequency domain. We show that, compared to other baseline algorithms, the proposed method significantly improves the estimated speech intelligibility and reduces the noise while preserving the spatial information of the binaural signals in acoustic situations with a single target speaker and isotropic noise of various types.