Dilated CNNs for Periodic Signal Processing: A Low-Complexity Approach

arXiv cs.LG / 4/24/2026

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Key Points

The paper addresses denoising and waveform estimation for periodic signals across domains like speech, music, medical diagnostics, radio, and sonar.
It proposes an efficient deep-learning method called R-DCNN that combines a dilated/convolutional neural network (DCNN) with re-sampling to work under strict power and resource constraints.
Unlike typical approaches that need separate training per observation, the method trains using only a single observation and generalizes to other signals by lightweight re-sampling that aligns time scales.
The authors report that, despite low computational complexity, R-DCNN achieves performance comparable to state-of-the-art classical AR-based methods and to conventional DCNNs trained individually.
The approach is positioned as deployment-friendly for resource-constrained environments without sacrificing denoising or estimation accuracy.

Abstract

Denoising of periodic signals and accurate waveform estimation are core tasks across many signal processing domains, including speech, music, medical diagnostics, radio, and sonar. Although deep learning methods have recently shown performance improvements over classical approaches, they require substantial computational resources and are usually trained separately for each signal observation. This study proposes a computationally efficient method based on DCNN and Re-sampling, termed R-DCNN, designed for operation under strict power and resource constraints. The approach targets signals with varying fundamental frequencies and requires only a single observation for training. It generalizes to additional signals via a lightweight resampling step that aligns time scales in signals with different frequencies to re-use the same network weights. Despite its low computational complexity, R-DCNN achieves performance comparable to state-of-the-art classical methods, such as autoregressive (AR)-based techniques, as well as conventional DCNNs trained individually for each observation. This combination of efficiency and performance makes the proposed method particularly well suited for deployment in resource-constrained environments without sacrificing denoising or estimation accuracy.