融入听觉空间感知的损失函数——综述
Loss functions incorporating auditory spatial perception in deep learning -- a review
AI总结：
本文综述了用于双耳重现的融合空间感知线索的损失函数，重点在于双耳信号的损失函数设计，强调了声源定位和房间响应等感知维度，指出定位线索如ITD和ILD是研究重点，而回声和房间声学属性研究较少。
摘要：
双耳重现旨在通过耳机提供高感知真实感的沉浸式空间音频。损失函数在优化和评估生成双耳信号的算法中起关键作用。然而，传统信号相关差异度量往往无法捕捉空间音频质量至关重要的感知特性。本文综述了近期整合与双耳重现相关的空间感知线索的损失函数。它聚焦于应用于双耳信号的损失函数，这些通常源自麦克风录音或Ambisonics信号，而排除基于房间脉冲响应的损失函数。受空间音频质量调查（SAQI）的指导，本文强调与声源定位和房间响应相关的感知维度，而排除一般频谱-时间属性。文献综述显示，研究重点集中在定位线索，如耳间时间差和水平差（ITD、ILD），而回声和其他房间声学属性在损失函数设计中仍较少被探索。最近的研究通过估计房间声学参数并开发捕捉房间特征的嵌入式表示，显示出其在神经网络训练中的潜在应用。本文最后强调了未来研究方向，即更加基于感知的损失函数，以更好地捕捉听者的空间体验。
英文摘要：
Binaural reproduction aims to deliver immersive spatial audio with high perceptual realism over headphones. Loss functions play a central role in optimizing and evaluating algorithms that generate binaural signals. However, traditional signal-related difference measures often fail to capture the perceptual properties that are essential to spatial audio quality. This review paper surveys recent loss functions that incorporate spatial perception cues relevant to binaural reproduction. It focuses on losses applied to binaural signals, which are often derived from microphone recordings or Ambisonics signals, while excluding those based on room impulse responses. Guided by the Spatial Audio Quality Inventory (SAQI), the review emphasizes perceptual dimensions related to source localization and room response, while excluding general spectral-temporal attributes. The literature survey reveals a strong focus on localization cues, such as interaural time and level differences (ITDs, ILDs), while reverberation and other room acoustic attributes remain less explored in loss function design. Recent works that estimate room acoustic parameters and develop embeddings that capture room characteristics indicate their potential for future integration into neural network training. The paper concludes by highlighting future research directions toward more perceptually grounded loss functions that better capture the listener's spatial experience.
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。
