论文标题
在线监督的声学系统识别利用预科的本地仿射子空间模型
Online Supervised Acoustic System Identification exploiting Prelearned Local Affine Subspace Models
论文作者
论文摘要
在本文中，我们提出了一种新颖的算法，用于通过利用有关房间脉冲响应（RIRS）的先验知识（RIR）的先验知识，以改善不利噪声场景中的块 - 连接监督的声学系统识别。该方法基于以下假设：未知RIR的变异性仅由几个物理参数控制，例如描述，例如源位置运动，因此仅限于低维歧管，该歧管由仿射子空间的结合而建模。仿射子空间的偏移和基础是通过无监督的聚类从训练数据中提取的，然后进行主成分分析。我们建议通过将其投影到最佳仿射子空间上的任何监督自适应过滤器的参数更新，该子空间是根据新颖的计算有效效率的相关证据进行选择的。所提出的方法可显着改善在不良噪声场景中最新算法的系统识别性能。
In this paper we present a novel algorithm for improved block-online supervised acoustic system identification in adverse noise scenarios by exploiting prior knowledge about the space of Room Impulse Responses (RIRs). The method is based on the assumption that the variability of the unknown RIRs is controlled by only few physical parameters, describing, e.g., source position movements, and thus is confined to a low-dimensional manifold which is modelled by a union of affine subspaces. The offsets and bases of the affine subspaces are learned in advance from training data by unsupervised clustering followed by Principal Component Analysis. We suggest to denoise the parameter update of any supervised adaptive filter by projecting it onto an optimal affine subspace which is selected based on a novel computationally efficient approximation of the associated evidence. The proposed method significantly improves the system identification performance of state-of-the-art algorithms in adverse noise scenarios.
