量化空间音频质量损伤
Quantifying Spatial Audio Quality Impairment
AI总结：
针对空间音频质量几何成分评估方法稀缺的问题，提出基于最小二乘优化与启发式的信号分解方法，分离空间误差并计算能量比指标，客观度量空间与非空间信号质量，对常见空间信号退化具有鲁棒性。
摘要：
空间音频质量是一个高度多维的概念，涉及环境、几何、解剖、心理和情境考量之间的诸多交互。然而，用于表征或评估空间音频质量几何成分的方法仍然稀缺，尽管这或许是空间音频质量中最不具主观性且易于量化的方面。通过考虑相对于参考信号的通道间时间差和电平差，可以构建一个信号模型来分离部分空间失真。通过结合使用最小二乘优化和启发式方法，我们提出了一种信号分解方法，以通道间增益泄漏和相对延迟变化的形式，从处理后的信号中分离出空间误差。这使得能够计算简单的能量比指标，从而在假设最少且无数据集依赖的情况下，提供空间与非空间信号质量的客观度量。实验表明，该方法对由音频压缩和音乐源分离等引入的常见空间信号退化具有鲁棒性。实现代码可在 https://github.com/karnwatcharasupat/spauq 获取。
英文摘要：
Spatial audio quality is a highly multifaceted concept, with many interactions between environmental, geometrical, anatomical, psychological, and contextual considerations. Methods for characterization or evaluation of the geometrical components of spatial audio quality, however, remain scarce, despite being perhaps the least subjective aspect of spatial audio quality to quantify. By considering interchannel time and level differences relative to a reference signal, it is possible to construct a signal model to isolate some of the spatial distortion. 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, in terms of interchannel gain leakages and changes in relative delays. This allows the computation of simple energy-ratio metrics, providing objective measures of spatial and non-spatial signal qualities, with minimal assumptions and no dataset dependency. Experiments demonstrate the robustness of the method against common spatial signal degradation introduced by, e.g., audio compression and music source separation. Implementation is available at https://github.com/karnwatcharasupat/spauq.
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。
