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A Deep Learning Approach to Digital Filter Parameter Estimation Based on Amplitude Responses
Author(s)
Date Issued
January 21, 2021
Type
Conference Paper
Abstract
This paper presents our attempt to tackle the problem of digital filter type and parameter estimation given a set of points sampled from a filter frequency response. We compared results from various multilayer perceptron and convolutional neural network configurations. The results suggest that a convolutional neural network generally produces faster convergence with a lower loss at the same number of epochs than a multilayer perceptron network. However, the maximum amplitude response error, which is the true performance metrics, can be comparable in some cases. A combination of multiple best-performing configurations for different tasks is used to assemble the final model.
Citation
Kst 2021 2021 13th International Conference Knowledge and Smart Technology, 243-248, 2021