Binaural Selective Attention Model for Target Speaker Extraction
<|reference_start|>Binaural Selective Attention Model for Target Speaker Extraction: 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.<|reference_end|>
@article{meng2024binaural,
    title={Binaural Selective Attention Model for Target Speaker Extraction},
    author={Hanyu Meng, Qiquan Zhang, Xiangyu Zhang, Vidhyasaharan Sethu,
  Eliathamby Ambikairajah},
    journal={arXiv preprint arXiv:2406.12236},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12236},
    primaryClass={eess.AS cs.SD eess.SP}
}