Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network
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en
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5
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Proceedings - 2023 19th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2023, pp. 348-352, Proceedings / IEEE Asia-Pacific Conference on Circuits and Systems
Abstract
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.Description
Publisher Copyright: © 2023 IEEE.
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Sinjanakhom, T & Chivapreecha, S 2024, Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network. in Proceedings - 2023 19th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2023. Proceedings / IEEE Asia-Pacific Conference on Circuits and Systems, IEEE, pp. 348-352, IEEE Asia Pacific Conference on Circuits and Systems, Hyderabad, India, 19/11/2023. https://doi.org/10.1109/APCCAS60141.2023.00084