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

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dc.contributor.authorSinjanakhom, Tantep
dc.contributor.authorChivapreecha, Sorawat
dc.date.accessioned2026-08-06T10:38:38Z
dc.date.available2026-08-06T10:38:38Z
dc.date.issued2023-01-01
dc.description.abstractThis 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.
dc.identifier.citationProceedings 2023 19th IEEE Asia Pacific Conference on Circuits and Systems Apccas 2023, 348-352, 2023
dc.identifier.doi10.1109/APCCAS60141.2023.00084
dc.identifier.other2-s2.0-85192804192
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/13658
dc.sourceProceedings 2023 19th IEEE Asia Pacific Conference on Circuits and Systems Apccas 2023
dc.subjectfilter design
dc.subjectgated recurrent units
dc.subjectreal-time
dc.subjectzero-phase
dc.titleReal-Time Zero-Phase Digital Filter Using Recurrent Neural Network
dc.typeConference Paper

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