Despite the recent proliferation of spatial audio technologies, the evaluation of spatial quality continues to rely on subjective listening tests, often requiring expert listeners. Based on the duplex theory of spatial hearing, it is possible to construct a signal model for frequency-independent spatial distortion by accounting for inter-channel time and level differences relative to a reference signal. By using a combination of least-square optimization and heuristics, we propose a signal decomposition method to isolate the spatial error from a processed signal. This allows the computation of simple energy-ratio metrics, providing objective measures of spatial and non-spatial signal qualities, with minimal assumption and no dataset dependency. Experiments demonstrate robustness of the method against common signal degradation as introduced by, e.g., audio compression and music source separation. Implementation is available at https://github.com/karnwatcharasupat/spauq.

翻译：尽管近年来空间音频技术迅速发展，空间质量的评估仍依赖于主观听力测试，且往往需要专家级听音者参与。基于双耳空间听觉的二元理论，可通过计算参考信号与待测信号通道间的时间差和电平差，构建频率无关的空间失真信号模型。本文结合最小二乘优化与启发式方法，提出一种信号分解技术从处理后信号中分离空间误差成分。该方法仅需最小化假设条件且不依赖数据集，即可通过计算简易的能量比指标，获得空间与非空间信号质量的客观度量。实验证明，该方法对音频压缩、音乐源分离等常见信号降质处理具有鲁棒性。相关实现代码已开源至https://github.com/karnwatcharasupat/spauq。