RADIA -- Radio Advertisement Detection with Intelligent Analytics
This addresses the need for efficient monitoring of radio advertisements to ensure compliance with contracts and enable competitive surveillance, though it appears incremental in applying existing AI methods to this specific domain.
The paper tackles the problem of automatically detecting advertisements in radio broadcasts by developing a novel technique that uses speech recognition and text classification, achieving an F1-macro score of 87.76 against a theoretical maximum of 89.33.
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.