Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network

Tantep Sinjanakhom, Sorawat Chivapreecha

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review


This paper proposes a method to design and implement a zero-phase digital filter that can run in a real-time system. Generally, zero-phase filters are designed for non-causal systems only as the time-reversal operations are required. Thus, the typical usage of these filters is for offline applications. For this reason, we propose a real-time zero-phase digital filter that is designed based on a recurrent neural network model, particularly the gated recurrent units. The model learns to perform zero-phase filtering by using training data made from the filtered signals that are generated by using the conventionally designed zero-phase filter. The original digital filter used to create the dataset is an IIR filter performing forward-backward filtering. The best trained model yields the mean absolute loss values at approximately 0.001 and can process at least 30 times faster than real-time. Furthermore, the trained model was implemented as a 3-band zero-phase graphic equalizer to exhibit one of its applications.

Original languageEnglish
Title of host publicationProceedings - 2023 19th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2023
Number of pages5
ISBN (Electronic)979-8-3503-8119-1
Publication statusPublished - 1 May 2024
MoE publication typeA4 Conference publication
EventIEEE Asia Pacific Conference on Circuits and Systems - Hyderabad, India
Duration: 19 Nov 202322 Nov 2023

Publication series

NameProceedings / IEEE Asia-Pacific Conference on Circuits and Systems
ISSN (Electronic)2768-3516


ConferenceIEEE Asia Pacific Conference on Circuits and Systems
Abbreviated titleAPCCAS


  • filter design
  • gated recurrent units
  • real-time
  • zero-phase


Dive into the research topics of 'Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network'. Together they form a unique fingerprint.

Cite this