A deep hybrid model for advertisements detection in broadcast TV and radio content. (28th April 2022) - Amrane, Abdesalam; Meziane, Abdelkrim; Rezgui, Abdelmounaam; Lebal, Abdelhamid
Media monitoring is essential for measuring the influence of companies over their consumers. It consists of building, reporting, and providing a full view of media sources in near real-time allowing to synthesise the data. Advertisement detection and classification in electronic media (TV and radio) is an essential part of a media monitoring system and is very useful for companies that work in a competitive environment. Advertisement detection entails many difficulties including unbalanced data, misclassification caused by outliers, and variation in loudness levels between TV/radio channels. To overcome these challenges, we propose a deep hybrid model for advertisement detection (DHM-ADS). We conduct several experiments by combining different methods: deep neural network models (ANN, CNN, and RNN) with dynamic time warping and multi-level deep neural networks such as autoencoders. The evaluation shows that the ANN classifier combined with an autoencoder gives the best result for advertisement detection in TV/radio broadcast even compared to the conventional framework 'DejaVu'.