基于HRTF的双耳目标说话人提取与现实验证
HRTF-guided Binaural Target Speaker Extraction with Real-World Validation
AI总结：
本文提出基于HRTF的双耳目标说话人提取框架，通过现实验证提升语音质量和可懂度，利用HRTF作为空间先验，增强双耳 cues 保留。
摘要：
本文提出一种基于头相关传输函数（HRTF）的双耳目标说话人提取（TSE）框架，用于从多个同时源混合物中提取目标说话人。与基于到达方向（DOA）估计或注册信号的传统TSE方法不同，本文方法利用听者的HRTF作为显式空间先验。该框架基于多通道深度盲源分离骨干，适应于双耳TSE场景。它在测量的HRTF数据上进行训练，实现跨听者泛化而非特定听者调优。通过将提取过程条件于HRTF衍生的空间信息，该方法保留双耳线索，同时提高语音质量和可懂度。所提框架的性能通过模拟和从头部和躯干模拟器（HATS）获得的真实录音进行验证。
英文摘要：
This paper presents a Head-Related Transfer Function (HRTF)-guided framework for binaural Target Speaker Extraction (TSE) from mixtures of concurrent sources. Unlike conventional TSE methods based on Direction of Arrival (DOA) estimation or enrollment signals, which often distort perceived spatial location, the proposed approach leverages the listener's HRTF as an explicit spatial prior. The proposed framework is built upon a multi-channel deep blind source separation backbone, adapted to the binaural TSE setting. It is trained on measured HRTFs from a diverse population, enabling cross-listener generalization rather than subject-specific tuning. By conditioning the extraction on HRTF-derived spatial information, the method preserves binaural cues while enhancing speech quality and intelligibility. The performance of the proposed framework is validated through simulations and real recordings obtained from a head and torso simulator (HATS).
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。
