具有双耳损失函数的SpatialNet用于在头部旋转下校正双耳信号匹配输出
SpatialNet with Binaural Loss Function for Correcting Binaural Signal Matching Outputs under Head Rotations
AI总结：
本研究提出结合深度学习与BSM-MagLS方法，通过SpatialNet网络和双耳损失函数提升双耳信号在头部旋转下的鲁棒性与准确性。
摘要：
双耳重现随着虚拟现实头戴设备、智能眼镜和头追踪耳机等设备的兴起而日益受到关注。实现这些系统中的准确双耳信号具有挑战性，因为它们通常使用任意麦克风阵列，具有有限的空间分辨率。为了解决早期BSM方法的局限性，开发了双耳信号匹配与幅度最小二乘（BSM-MagLS）方法，以提高高频再现和头部旋转下的再现效果。然而，其准确性在头部旋转增加时仍会下降，导致空间和音色伪影，特别是当虚拟听者的耳朵远离最近的麦克风时。在本工作中，我们提出将深度学习与BSM-MagLS相结合，以减轻这些退化。基于SpatialNet网络的后处理框架被采用，利用其有效处理空间信息的能力，并通过信号级损失和从人类双耳听觉理论模型中衍生出的感知驱动双耳损失进行引导。该方法的有效性在六麦克风半圆形阵列的模拟研究中得到检验，显示其在各种头部旋转下的鲁棒性。这些发现进一步在不同混响声学环境和头部旋转下的听觉实验中得到研究，证明所提出的框架有效减轻了BSM-MagLS的退化，并在显著的头部旋转下提供了稳健的校正。
英文摘要：
Binaural reproduction is gaining increasing attention with the rise of devices such as virtual reality headsets, smart glasses, and head-tracked headphones. Achieving accurate binaural signals with these systems is challenging, as they often employ arbitrary microphone arrays with limited spatial resolution. The Binaural Signals Matching with Magnitude Least-Squares (BSM-MagLS) method was developed to address limitations of earlier BSM formulations, improving reproduction at high frequencies and under head rotation. However, its accuracy still degrades as head rotation increases, resulting in spatial and timbral artifacts, particularly when the virtual listener's ear moves farther from the nearest microphones. In this work, we propose the integration of deep learning with BSM-MagLS to mitigate these degradations. A post-processing framework based on the SpatialNet network is employed, leveraging its ability to process spatial information effectively and guided by both signal-level loss and a perceptually motivated binaural loss derived from a theoretical model of human binaural hearing. The effectiveness of the approach is investigated in a simulation study with a six-microphone semicircular array, showing its ability to perform robustly across head rotations. These findings are further studied in a listening experiment across different reverberant acoustic environments and head rotations, demonstrating that the proposed framework effectively mitigates BSM-MagLS degradations and provides robust correction across substantial head rotations.
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