基于卡尔曼滤波和自适应脉冲响应子空间模型的在线声学系统辨识
Online Acoustic System Identification Exploiting Kalman Filtering and an Adaptive Impulse Response Subspace Model
摘要：
我们介绍了一种新的算法在线估计的声脉冲响应（AIR），它允许快速收敛，利用先验知识的基本结构的AIR。所提出的方法假设的变化的空气的声学场景被限制到一个低维流形，这是嵌入在一个高维空间的可能的空气估计。我们讨论了各种方法来局部近似的空气歧管的仿射子空间，假设切超平面的流形。这些模型假设的有效性进行了验证模拟数据。随后，我们描述了如何学习模型可以用来提高在线空气估计投影到一个自适应估计的子空间。确定子空间的参数是从当前AIR估计的局部邻域中的训练样本中学习的。这允许系统辨识算法受益于声学场景中的先前估计。为了评估训练数据AIR与当前AIR估计的接近程度，我们引入了欧几里德距离的概率扩展，其提高了具有相关激励信号的应用的性能。此外，我们描述了如何模型的缺陷，可以解决软投影的空气估计。该算法具有显着更快的收敛性能相比，一个高性能的最先进的算法。此外，我们表现出改善的稳态性能的语音激励系统辨识的情况下遭受高水平的干扰噪声和非唯一的解决方案。
英文摘要：
We introduce a novel algorithm for online estimation of acoustic impulse responses (AIRs) which allows for fast convergence by exploiting prior knowledge about the fundamental structure of AIRs. The proposed method assumes that the variability of AIRs of an acoustic scene is confined to a low-dimensional manifold which is embedded in a high-dimensional space of possible AIR estimates. We discuss various approaches to locally approximate the AIR manifold by affine subspaces which are assumed to be tangential hyperplanes to the manifold. The validity of these model assumptions is verified for simulated data. Subsequently, we describe how the learned models can be used to improve online AIR estimates by projecting them onto an adaptively estimated subspace. The parameters determining the subspace are learned from training samples in a local neighbourhood to the current AIR estimate. This allows the system identification algorithm to benefit from preceding estimates in the acoustic scene. To assess the proximity of training data AIRs to the current AIR estimate, we introduce a probabilistic extension of the Euclidean distance which improves the performance for applications with correlated excitation signals. Furthermore, we describe how model imperfections can be tackled by a soft projection of the AIR estimates. The proposed algorithm exhibits significantly faster convergence properties in comparison to a high-performance state-of-the-art algorithm. Furthermore, we show an improved steady-state performance for speech-excited system identification scenarios suffering from high-level interfering noise and nonunique solutions.
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