ABSTRACT
The incredible growth in available news content has been met with steeply increasing demand for news amongst the general population. The 24/7 news cycle gives people an awareness of events, activities and decisions that may have an impact on them (e.g. the latest updates on the COVID-19 outbreak). Despite the flourish of social networks, recent research suggests radio and especially TV are still the main sources of news for many people. However, unlike in social media, the content aired on radio and TV requires people to listen to every single advertisement and music (for radio) before consuming the next item. For this reason, media monitoring companies have to dedicate considerable amount of resources on processing or manually filtering the advertising content (which is blended with the actual news). Often their clients still receive ads. To mitigate this problem, in this paper, we propose No2Ads, an autoregressive deep convolutional neural network (CNN) model that is trained on over 500 h of human annotated training samples to remove ads and music from broadcast content. No2Ads reached very high performance results in our tests, achieving 97% and 95% in precision and recall on detecting ads/music for radio channels;95% precision and 98% recall for TV channels. Between March to September 2021, across 261 radio and TV channels in Australia and New Zealand, No2Ads has detected and filtered out 22,161 h of all captured broadcast content as either advertisements or music. © 2022, Springer Nature Switzerland AG.