标题： 深度学习用于个性化双耳音频再现 显示英文标题

标题： Deep Learning for Personalized Binaural Audio Reproduction

摘要： 个性化双耳音频再现是实现真实空间定位、声音外化和沉浸式聆听的基础，直接影响用户体验和听觉努力程度。 本综述回顾了该任务的深度学习最新进展，并根据生成机制将其分为两种范式：显式个性化滤波和端到端渲染。 显式方法从稀疏测量、形态特征或环境线索中预测个性化的头部相关传递函数（HRTFs），然后在传统渲染流程中使用它们。 端到端方法通过其他输入（如视觉、文本或参数指导）辅助，将源信号直接映射到双耳信号，并在模型内部学习个性化。 我们还总结了该领域的主要数据集和评估指标，以支持公平和可重复的比较。 最后，我们讨论了这些技术带来的关键应用、当前的技术限制以及基于深度学习的空间音频系统的潜在研究方向。 显示英文摘要

摘要： Personalized binaural audio reproduction is the basis of realistic spatial localization, sound externalization, and immersive listening, directly shaping user experience and listening effort. This survey reviews recent advances in deep learning for this task and organizes them by generation mechanism into two paradigms: explicit personalized filtering and end-to-end rendering. Explicit methods predict personalized head-related transfer functions (HRTFs) from sparse measurements, morphological features, or environmental cues, and then use them in the conventional rendering pipeline. End-to-end methods map source signals directly to binaural signals, aided by other inputs such as visual, textual, or parametric guidance, and they learn personalization within the model. We also summarize the field's main datasets and evaluation metrics to support fair and repeatable comparison. Finally, we conclude with a discussion of key applications enabled by these technologies, current technical limitations, and potential research directions for deep learning-based spatial audio systems.