基于端到端深度学习的频域自适应系统辨识自适应控制
End-To-End Deep Learning-Based Adaptation Control for Frequency-Domain Adaptive System Identification
摘要：
针对频域自适应系统辨识提出了一种新的基于端到端深度学习的自适应控制算法。所提出的方法利用深度神经网络将观察到的信号特征映射到控制滤波器自适应的相应步长。通过最小化自适应滤波器的平均归一化系统距离，以端到端的方式优化网络参数。这避免了基于模型的自适应控制所需的显式信号功率谱密度估计的需要以及处理模型不准确性的进一步辅助机制。该算法实现了快速收敛和鲁棒的稳态性能的情况下，其特征在于高电平，非白色和非平稳的加性噪声信号，突然的环境变化和额外的模型不准确。
英文摘要：
We present a novel end-to-end deep learning-based adaptation control algorithm for frequency-domain adaptive system identification. The proposed method exploits a deep neural network to map observed signal features to corresponding step-sizes which control the filter adaptation. The parameters of the network are optimized in an end-to-end fashion by minimizing the average normalized system distance of the adaptive filter. This avoids the need of explicit signal power spectral density estimation as required for model-based adaptation control and further auxiliary mechanisms to deal with model inaccuracies. The proposed algorithm achieves fast convergence and robust steady-state performance for scenarios characterized by high-level, non-white and non-stationary additive noise signals, abrupt environment changes and additional model inaccuracies.
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