# BASEN: Time-Domain Brain-Assisted Speech Enhancement Network with Convolutional Cross Attention in Multi-talker Conditions

arXiv: https://arxiv.org/abs/2305.09994 · HTML: https://mlsanity.com/paper/2305.09994/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 39 references that are in the map.
- First posted on arXiv 17 May 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [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)
- [TasNet: time-domain audio separation network for real-time, single-channel speech separation](https://mlsanity.com/paper/1711.00541/) (2017)
- [An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation](https://mlsanity.com/paper/2008.09586/) (2020)
- [VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking](https://mlsanity.com/paper/1810.04826/) (2018)
- [Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network](https://mlsanity.com/paper/2112.02321/) (2021)
- [EEG-based Auditory Attention Decoding: Towards Neuro-Steered Hearing Devices](https://mlsanity.com/paper/2008.04569/) (2020)
- [On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement](https://mlsanity.com/paper/1909.01019/) (2019)
- [Dense CNN with Self-Attention for Time-Domain Speech Enhancement](https://mlsanity.com/paper/2009.01941/) (2020)
- [On The Compensation Between Magnitude and Phase in Speech Separation](https://mlsanity.com/paper/2108.05470/) (2021)
- [Speech Synthesis using EEG](https://mlsanity.com/paper/2002.12756/) (2020)
- [Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural Speech Enhancement](https://mlsanity.com/paper/2205.00206/) (2022)

## Built on by (most cited first, 4 of 4)

- [NeuroSpex: Neuro-Guided Speaker Extraction with Cross-Modal Attention](https://mlsanity.com/paper/2409.02489/) (2024)
- [Brainprint-Modulated Target Speaker Extraction](https://mlsanity.com/paper/2509.17883/) (2025)
- [TFGA-Net: Temporal-Frequency Graph Attention Network for Brain-Controlled Speaker Extraction](https://mlsanity.com/paper/2510.12275/) (2025)
- [Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training](https://mlsanity.com/paper/2606.24164/) (2026)

## Abstract (arXiv)

Time-domain single-channel speech enhancement (SE) still remains challenging to extract the target speaker without any prior information on multi-talker conditions. It has been shown via auditory attention decoding that the brain activity of the listener contains the auditory information of the attended speaker. In this paper, we thus propose a novel time-domain brain-assisted SE network (BASEN) incorporating electroencephalography (EEG) signals recorded from the listener for extracting the target speaker from monaural speech mixtures. The proposed BASEN is based on the fully-convolutional time-domain audio separation network. In order to fully leverage the complementary information contained in the EEG signals, we further propose a convolutional multi-layer cross attention module to fuse the dual-branch features. Experimental results on a public dataset show that the proposed model outperforms the state-of-the-art method in several evaluation metrics. The reproducible code is available at https://github.com/jzhangU/Basen.git.