Evaluation of Machine Learning Approaches for Anomaly Analysis in VHF Band II Spectrum Monitoring
Year: 2026
Type: article
Abstract: This paper presents a study of three machine-learning approaches for anomaly analysis in spectrum-monitoring data from VHF Band II radio broadcasting. Spectrum anomalies are defined as deviations from expected spectral behavior, including unexpected signal emissions, interference, and irregular variations in signal characteristics. A supervised YOLO-based object detector was evaluated for anomaly localization using independently produced expert annotations, while convolutional autoencoder-based clustering and long short-term memory (LSTM) prediction were investigated for exploratory spectral and temporal analysis, respectively. The supervised experiments examined both within-annotator and cross-annotator performance, whereas the unsupervised and temporal approaches were assessed qualitatively. The autoencoder-based approach revealed recurring spectral structures in latent space, and the LSTM model identified deviations from the recent behavior of individual broadcast transmissions. The principal quantitative results showed within-annotator F1 scores ranging from 0.85 to 0.94, compared with 0.34 to 0.77 in cross-annotator evaluations. These findings indicate that the reported anomaly localization performance is sensitive to the reference annotations, highlighting the importance of annotation consistency and independent evaluation in spectrum-monitoring applications.
Source: Applied Sciences
DOI: 10.3390/app162211138
Institutions: Széchenyi István University, Eötvös Loránd University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7225752009
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