基于预学习局部仿射子空间模型的在线监督声学系统辨识
Online Supervised Acoustic System Identification exploiting Prelearned Local Affine Subspace Models
摘要：
在本文中，我们提出了一种新的算法改进块在线监督声学系统辨识在不利的噪声情况下，利用先验知识的空间的房间脉冲响应（RIR）。该方法基于这样的假设，即未知RIR的可变性仅由少数物理参数控制，例如，源位置移动，因此被限制到由仿射子空间的并集建模的低维流形。仿射子空间的偏移量和基是通过无监督聚类和主成分分析从训练数据中预先学习的。我们建议去噪的任何监督自适应滤波器的参数更新投影到一个最佳的仿射子空间，这是基于一个新的计算效率的近似相关联的证据。所提出的方法显着提高了系统辨识性能的最先进的算法在不利的噪声情况下。
英文摘要：
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.
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