用于目标说话人提取的双耳选择性注意模型
Binaural Selective Attention Model for Target Speaker Extraction
AI总结：
针对鸡尾酒会场景下的目标说话人提取问题，受人类双耳选择性听觉启发，提出基于FaSNet、引入多头选择性注意模块的双耳时域模型，对比两种双耳交互方法，在无回声双说话人测试中取得优于现有模型的最优性能。
摘要：
人类能够在鸡尾酒会场景中选择性地专注于目标说话人，这种卓越能力得益于双耳音频处理。本文提出了一种基于Filter-and-Sum Network（FaSNet，滤波求和网络）的双耳时域目标说话人提取模型。受人类选择性听觉的启发，该模型通过基于多头注意力的选择性注意模块，将目标说话人嵌入引入到分离器中。本文还对比了两种双耳交互方法：时域信号的余弦相似度，以及学习到的频谱表示中的通道间相关性。实验结果表明，所提模型性能优于单声道配置以及当前最先进的多通道目标说话人提取模型，在无回声双说话人测试配置下，取得了18.52 dB SI-SDR、19.12 dB SDR和3.05 PESQ的最优性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
