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.

翻译：无线电广告仍然是现代营销策略中不可或缺的一部分，其吸引力和定向覆盖的潜力毋庸置疑。然而，无线电广播时段的动态特性以及多电台广告的日益增多，使得高效监测广告播出系统变得必要。本研究提出了一种新颖的自动化无线电广告检测技术，该技术融合了先进的语音识别和文本分类算法。RadIA方法通过无需预先了解广播内容的方式，超越了传统方法。这一贡献使得能够检测即兴播报和新推出的广告，为无线电广播中的广告检测提供了全面的解决方案。实验结果表明，该模型在精心分割和标注的文本数据上训练后，其F1-macro得分达到了87.76，而理论最高得分为89.33。本文还探讨了超参数的选择及其对模型性能的影响。这项研究展示了其在确保广告播出合同合规性以及提供竞争性监控方面的潜力。这项开创性研究可能从根本上改变无线电广告的监测方式，并为营销优化开辟新的途径。