RADIA -- Radio Advertisement Detection with Intelligent Analytics
Jorge Álvarez Juan Carlos Armenteros Camilo Torrón Miguel Ortega-Martín Alfonso Ardoiz Óscar García Ignacio Arranz Íñigo Galdeano Ignacio Garrido Adrián Alonso Fernando Bayón Oleg Vorontsov
Radio advertising remains an integral part of modern marketing strategies,
with its appeal and potential for targeted reach undeniably effective. However,
the dynamic nature of radio airtime and the rising trend of multiple radio
spots necessitates an efficient system for monitoring advertisement broadcasts.
This study investigates a novel automated radio advertisement detection
technique incorporating advanced speech recognition and text classification
algorithms. RadIA's approach surpasses traditional methods by eliminating the
need for prior knowledge of the broadcast content. This contribution allows for
detecting impromptu and newly introduced advertisements, providing a
comprehensive solution for advertisement detection in radio broadcasting.
Experimental results show that the resulting model, trained on carefully
segmented and tagged text data, achieves an F1-macro score of 87.76 against a
theoretical maximum of 89.33. This paper provides insights into the choice of
hyperparameters and their impact on the model's performance. This study
demonstrates its potential to ensure compliance with advertising broadcast
contracts and offer competitive surveillance. This groundbreaking research
could fundamentally change how radio advertising is monitored and open new
doors for marketing optimization.