# A Novel Binaural Cue Preservation Loss for DNN-Based Binaural Speech Enhancement

arXiv: https://arxiv.org/abs/2608.16299 · HTML: https://mlsanity.com/paper/2608.16299/

## Key facts

- No citing papers in the map yet (data through 20 Oct 2026).
- Placed in Speech enhancement and noise suppression, one of 11 directions in Speech and audio language models.
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 17 Aug 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, 11 of 11)

- [SDR - half-baked or well done?](https://mlsanity.com/paper/1811.02508/) (2018)
- [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)
- [EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation](https://mlsanity.com/paper/2406.06185/) (2024)
- [Towards efficient models for real-time deep noise suppression](https://mlsanity.com/paper/2101.09249/) (2021)
- [DeepFilterNet2: Towards Real-Time Speech Enhancement on Embedded Devices for Full-Band Audio](https://mlsanity.com/paper/2205.05474/) (2022)
- [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 Lightweight Fourier-based Network for Binaural Speech Enhancement with Spatial Cue Preservation](https://mlsanity.com/paper/2509.14076/) (2025)

## Abstract (arXiv)

Binaural speech enhancement for hearing aids aims to reduce noise while preserving the interaural cues needed for spatial localization. Although deep neural network-based methods achieve strong noise reduction, they often distort the rela- tionship between the left and right signals. In this paper, we propose two novel binaural cue preservation losses. First, a binaural reconstruction error loss that directly penalizes masking-induced distortion in the relationship between the left and right spectra, providing a more direct measure of the binaural consistency than conventional separate interaural level differences (ILD) and interaural phase differences (IPD) errors as in prior work. Second, a binaural cue loss that jointly models ILD and IPD to better preserve the binaural structure. Experimental results show that both proposed losses maintain strong noise reduction performance and reduce masking- induced distortion compared to the state-of-the-art baseline cue loss, while the second proposed joint binaural cue loss also outperforms the baseline in ILD preservation.