2021/06/02 by Thomas Haubner, Haubner, Thomas, Andreas Brendel +3
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Signal Processing (eess.SP) #Sound (cs.SD) #Structural Health Monitoring Techniques #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2106.01262
openalex publication_date 2021/06/02 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.