DySpect-Net: Neural Network Predicting Spectrum From Non-Uniformly Sampled Data

Accurate spectral estimation from irregularly sampled signals is a long-standing challenge in signal processing, particularly in biomedical applications. Traditional methods, such as the Fast Fourier Transform (FFT), rely on resampling, which introduces artefacts and computational inefficiencies. To address this, we propose DySpect-Net (Dynamic Spectrum Network), a neural network-based model that directly predicts spectral representations from non-uniformly sampled data, eliminating the need for resampling. Our approach is validated using synthetic datasets that mimic real-world sampling irregularities. Unlike conventional spectral estimation methods, DySpect-Net learns to recognise and reconstruct dominant frequency components dynamically. We introduce FAVE (Frequency Absolute Variance Error), a novel evaluation metric designed to quantify spectral peak alignment accuracy, outperforming conventional metrics such as Mean Squared Error (MSE). Comparative analysis demonstrates that DySpect-Net performs superior spectral estimation, particularly in low-sampling-rate conditions, with fewer distortions than FFT-based approaches. The proposed model has broad applications in biomedical signal analysis, e.g. remote photoplethysmography (rPPG), and other domains where sampling irregularities compromise spectral fidelity. Future research will focus on integrating FAVE as a loss function to enhance spectral reconstruction accuracy.