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.

本研究提出一种新的自动广播广告检测技术，通过运用先进的语音识别和文本分类算法，不需要事先了解广播内容即可检测即席广告和新引入的广告，实现了对广播中广告的全面检测，为广告播放监测提供了全面解决方案。实验证明，该模型在经过仔细分割和标记的文本数据上训练，F1-macro 得分达到了 87.76，较理论最大值 89.33 略低。本研究对超参数的选择及其对模型性能的影响提供了深入理解，展示了该技术确保广告播放合同合规性和提供竞争监测的潜力。这项开创性的研究可能从根本上改变广播广告的监测方式，并为市场营销优化开辟了新的可能性。