利用个体化HRTF的双耳目标说话人提取
Binaural Target Speaker Extraction using Individualized HRTF
AI总结：
本文提出利用个体化HRTF的双耳目标说话人提取方法，通过复值神经网络在无回声和混响条件下均实现高效提取，保留双耳线索并提升语音清晰度。
摘要：
在多个同时说话者存在的环境中，我们解决了双耳目标说话人提取的问题。我们提出了一种新的方法，利用个体听众的头相关传输函数（HRTF）来隔离目标说话人。所提出的方法是说话人无关的，因为它不依赖于说话人嵌入。我们采用了一个完全复值神经网络，直接在混合音频信号的复值短时傅里叶变换（STFT）上操作，并将其与基于实虚（RI）的神经网络进行比较，证明了前者的优点。我们首先在无回声、无噪声的环境中评估该方法，实现了出色的提取性能，同时保持了目标信号的双耳线索。然后我们将评估扩展到混响条件。我们的方法证明了其鲁棒性，在保持语音清晰度和源方向性的同时，同时减少了混响。与现有双耳目标说话人提取（TSE）方法的比较分析表明，所提出的方法在降噪和感知质量方面与最先进的技术相当，同时在保持双耳线索方面具有明显优势。Demo-page: https://bi-ctse-hrtf.github.io
英文摘要：
In this work, we address the problem of binaural target-speaker extraction in the presence of multiple simultane-ous talkers. We propose a novel approach that leverages the individual listener's Head-Related Transfer Function (HRTF) to isolate the target speaker. The proposed method is speaker-independent, as it does not rely on speaker embeddings. We employ a fully complex-valued neural network that operates directly on the complex-valued Short-Time Fourier transform (STFT) of the mixed audio signals, and compare it to a Real-Imaginary (RI)-based neural network, demonstrating the advantages of the former. We first evaluate the method in an anechoic, noise-free scenario, achieving excellent extraction performance while preserving the binaural cues of the target signal. We then extend the evaluation to reverberant conditions. Our method proves robust, maintaining speech clarity and source directionality while simultaneously reducing reverberation. A comparative analysis with existing binaural Target Speaker Extraction (TSE) methods shows that the proposed approach achieves performance comparable to state-of-the-art techniques in terms of noise reduction and perceptual quality, while providing a clear advantage in preserving binaural cues. Demo-page: https://bi-ctse-hrtf.github.io
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。
