用核岭回归实现时域声场估计,可融合时空先验提升精度。
Time-domain sound field estimation using kernel ridge regression
- 将核岭回归拓展至时域,支持直接计算时域声场。
- 结合时域与方向性先验,实测性能显著提升。
- 适合需建模声场动态特性的音频场景研究者。
基于核岭回归的声场估计方法已被证明有效,能够严格满足物理特性,并融入如声场指向性等先验知识。然而,现有方法仅适用于单频声场,限制了数据类型和先验信息的使用。本文将核岭回归推广至离散时间声场,提出一种可闭式求解的时域声场估计方法,保证结果物理可实现,并利用声场的时间特性提升估计性能。通过引入时域数据加权,结合房间混响脉冲响应的时间特性先验,实验表明估计性能得到改善。同时,在模拟与真实数据上验证了时域加权与方向加权的协同作用,充分利用了房间混响脉冲响应的时空特性。该理论框架使核岭回归能解决更广泛的时域声场估计问题,不再局限于对各频率分别处理的频域方法。
原文摘要 · Abstract (English)
Sound field estimation methods based on kernel ridge regression have proven effective, allowing for strict enforcement of physical properties, in addition to the inclusion of prior knowledge such as directionality of the sound field. These methods have been formulated for single-frequency sound fields, restricting the types of data and prior knowledge that can be used. In this paper, the kernel ridge regression approach is generalized to consider discrete-time sound fields. The proposed method provides time-domain sound field estimates that can be computed in closed form, are guaranteed to be physically realizable, and for which time-domain properties of the sound fields can be exploited to improve estimation performance. Exploiting prior information on the time-domain behaviour of room impulse responses, the estimation performance of the proposed method is shown to be improved using a time-domain data weighting, demonstrating the usefulness of the proposed approach. It is further shown using both simulated and real data that the time-domain data weighting can be combined with a directional weighting, exploiting prior knowledge of both spatial and temporal properties of the room impulse responses. The theoretical framework of the proposed method enables solving a broader class of sound field estimation problems using kernel ridge regression where it would be required to consider the time-domain response rather than the frequency-domain response of each frequency separately.
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