# Real-time binaural speech separation with preserved spatial cues

arXiv: https://arxiv.org/abs/2002.06637 · HTML: https://mlsanity.com/paper/2002.06637/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2020.

## Built on by (most cited first, 11 of 11)

- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [Look Once to Hear: Target Speech Hearing with Noisy Examples](https://mlsanity.com/paper/2405.06289/) (2024)
- [Hybrid Neural Networks for On-device Directional Hearing](https://mlsanity.com/paper/2112.05893/) (2021)
- [Points2Sound: From mono to binaural audio using 3D point cloud scenes](https://mlsanity.com/paper/2104.12462/) (2021)
- [Learning to Separate Voices by Spatial Regions](https://mlsanity.com/paper/2207.04203/) (2022)
- [Spatial Speech Translation: Translating Across Space With Binaural Hearables](https://mlsanity.com/paper/2504.18715/) (2025)
- [A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks](https://mlsanity.com/paper/2112.04939/) (2021)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Online Self-Attentive Gated RNNs for Real-Time Speaker Separation](https://mlsanity.com/paper/2106.13493/) (2021)
- [A Lightweight and Real-Time Binaural Speech Enhancement Model with Spatial Cues Preservation](https://mlsanity.com/paper/2409.12444/) (2024)
- [Speech Separation for Hearing-Impaired Children in the Classroom](https://mlsanity.com/paper/2511.07677/) (2025)

## Abstract (arXiv)

Deep learning speech separation algorithms have achieved great success in improving the quality and intelligibility of separated speech from mixed audio. Most previous methods focused on generating a single-channel output for each of the target speakers, hence discarding the spatial cues needed for the localization of sound sources in space. However, preserving the spatial information is important in many applications that aim to accurately render the acoustic scene such as in hearing aids and augmented reality (AR). Here, we propose a speech separation algorithm that preserves the interaural cues of separated sound sources and can be implemented with low latency and high fidelity, therefore enabling a real-time modification of the acoustic scene. Based on the time-domain audio separation network (TasNet), a single-channel time-domain speech separation system that can be implemented in real-time, we propose a multi-input-multi-output (MIMO) end-to-end extension of TasNet that takes binaural mixed audio as input and simultaneously separates target speakers in both channels. Experimental results show that the proposed end-to-end MIMO system is able to significantly improve the separation performance and keep the perceived location of the modified sources intact in various acoustic scenes.