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.

翻译：我们提出了一种新的基于学习的深端适应控制算法,用于确定频率域适应系统。拟议方法利用一个深神经网络,将观测到的信号特征映射成相应的控制过滤器适应的阶梯尺寸。网络参数以端到端的方式优化,尽量减少适应过滤器的平均正常系统距离。这避免了模型适应控制所需的明确信号光谱密度估计和处理模型不准确的进一步辅助机制。拟议的算法在高层次、非白色和非静止添加噪声信号、突发环境变化和更多模型不准确的情景中实现快速趋同和稳健的状态性能。