  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.
