We present a noise-robust adaptation control strategy for block-online supervised acoustic system identification by exploiting a noise dictionary. The proposed algorithm takes advantage of the pronounced spectral structure which characterizes many types of interfering noise signals. We model the noisy observations by a linear Gaussian Discrete Fourier Transform-domain state space model whose parameters are estimated by an online generalized Expectation-Maximization algorithm. Unlike all other state-of-the-art approaches we suggest to model the covariance matrix of the observation probability density function by a dictionary model. We propose to learn the noise dictionary from training data, which can be gathered either offline or online whenever the system is not excited, while we infer the activations continuously. The proposed algorithm represents a novel machine-learning based approach to noise-robust adaptation control which allows for faster convergence in applications characterized by high-level and non-stationary interfering noise signals and abrupt system changes.

中文翻译：

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利用噪声字典进行有监督声学系统识别的噪声鲁棒自适应控制

我们通过利用噪声字典提出了一种用于块在线监督声学系统识别的噪声鲁棒自适应控制策略。所提出的算法利用了表征多种干扰噪声信号的显着频谱结构。我们通过线性高斯离散傅立叶变换域状态空间模型对噪声观测进行建模，该模型的参数由在线广义期望最大化算法估计。与所有其他最先进的方法不同，我们建议通过字典模型对观测概率密度函数的协方差矩阵进行建模。我们建议从训练数据中学习噪声字典，只要系统不兴奋就可以离线或在线收集，同时我们不断地推断激活。