Zoltán Németh

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 $F_1$ 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.