1-D Convolutional Neural Networks for Signal Processing Applications

By Serkan Kiranyaz, Turker Ince, Osama Abdeljaber, Onur Avcı, Moncef Gabbouj. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2019

297 citations recorded by the source corpus.

1D Convolutional Neural Networks (CNNs) have recently become the state-of-the-art technique for crucial signal processing applications such as patient-specific ECG classification, structural health monitoring, anomaly detection in power electronics circuitry and motor-fault detection. This is an expected outcome as there are numerous advantages of using an adaptive and compact 1D CNN instead of a conventional (2D) deep counterparts. First of all, compact 1D CNNs can be efficiently trained with a limited dataset of 1D signals while the 2D deep CNNs, besides requiring 1D to 2D data transformation, usually need datasets with massive size, e.g., in the "Big Data" scale in order to prevent the well-known "overfitting" problem. 1D CNNs can directly be applied to the raw signal (e.g., current, voltage, vibration, etc.) without requiring any pre- or post-processing such as feature extraction, selection, dimension reduction, denoising, etc. Furthermore, due to the simple and compact configuration of such adaptive 1D CNNs that perform only linear 1D convolutions (scalar multiplications and additions), a real-time and low-cost hardware implementation is feasible. This paper reviews the major signal processing applications of compact 1D CNNs with a brief theoretical background. We will present their state-of-the-art performances and conclude with focusing on some major properties. Keywords – 1-D CNNs, Biomedical Signal Processing, SHM

Subfields: Cardiology and Cardiovascular Medicine, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/28a7777e907d617244d21bf4d652040da73304ff

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In Control and Systems Engineering

Citations in the corpus

Fusing Physics-based and Deep Learning Models for Prognostics (2020, background): “7) have also achieved excellent results in signal processing applications [41].”
A CNN-RNN Framework for Crop Yield Prediction (2019, methodology): “Similar convolutional models have been widely used in various application domains and found to be effective in improving prediction accuracy (Ince et al., 2016; Borovykh et al., 2017; Kiranyaz et al., 2019).”