# SAGRNN: Self-Attentive Gated RNN for Binaural Speaker Separation with Interaural Cue Preservation

arXiv: https://arxiv.org/abs/2009.01381 · HTML: https://mlsanity.com/paper/2009.01381/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

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

- [MIMO Self-attentive RNN Beamformer for Multi-speaker Speech Separation](https://mlsanity.com/paper/2104.08450/) (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)
- [Neural Brain Fields: A NeRF-Inspired Approach for Generating Nonexistent EEG Electrodes](https://mlsanity.com/paper/2601.00012/) (2025)
- [Online Self-Attentive Gated RNNs for Real-Time Speaker Separation](https://mlsanity.com/paper/2106.13493/) (2021)

## Abstract (arXiv)

Most existing deep learning based binaural speaker separation systems focus on producing a monaural estimate for each of the target speakers, and thus do not preserve the interaural cues, which are crucial for human listeners to perform sound localization and lateralization. In this study, we address talker-independent binaural speaker separation with interaural cues preserved in the estimated binaural signals. Specifically, we extend a newly-developed gated recurrent neural network for monaural separation by additionally incorporating self-attention mechanisms and dense connectivity. We develop an end-to-end multiple-input multiple-output system, which directly maps from the binaural waveform of the mixture to those of the speech signals. The experimental results show that our proposed approach achieves significantly better separation performance than a recent binaural separation approach. In addition, our approach effectively preserves the interaural cues, which improves the accuracy of sound localization.