# Binaural Selective Attention Model for Target Speaker Extraction

arXiv: https://arxiv.org/abs/2406.12236 · HTML: https://mlsanity.com/paper/2406.12236/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Blind Estimation of Sub-band Acoustic Parameters from Ambisonics Recordings using Spectro-Spatial Covariance Features](https://mlsanity.com/paper/2411.03172/) (2024)
- [Target Speaker Extraction through Comparing Noisy Positive and Negative Audio Enrollments](https://mlsanity.com/paper/2502.16611/) (2025)

## Abstract (arXiv)

The remarkable ability of humans to selectively focus on a target speaker in cocktail party scenarios is facilitated by binaural audio processing. In this paper, we present a binaural time-domain Target Speaker Extraction model based on the Filter-and-Sum Network (FaSNet). Inspired by human selective hearing, our proposed model introduces target speaker embedding into separators using a multi-head attention-based selective attention block. We also compared two binaural interaction approaches -- the cosine similarity of time-domain signals and inter-channel correlation in learned spectral representations. Our experimental results show that our proposed model outperforms monaural configurations and state-of-the-art multi-channel target speaker extraction models, achieving best-in-class performance with 18.52 dB SI-SDR, 19.12 dB SDR, and 3.05 PESQ scores under anechoic two-speaker test configurations.