A deep hybrid model for advertisements detection in broadcast TV and radio content
by Abdesalam Amrane; Abdelkrim Meziane; Abdelmounaam Rezgui; Abdelhamid Lebal
International Journal of Computational Vision and Robotics (IJCVR), Vol. 12, No. 4, 2022

Abstract: 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'.

Online publication date: Mon, 04-Jul-2022

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