Title: A deep hybrid model for advertisements detection in broadcast TV and radio content

Authors: Abdesalam Amrane; Abdelkrim Meziane; Abdelmounaam Rezgui; Abdelhamid Lebal

Addresses: Department of Computer Science, Faculty of Exact Sciences, University of Bejaia, 06000, Bejaia, Algeria; Research Center on Scientific and Technical Information (CERIST), 16030, Algiers, Algeria; School of Information Technology, Illinois State University, Campus Box 5150 – Normal, IL 61790-5150, USA ' Research Center on Scientific and Technical Information (CERIST), 16030, Algiers, Algeria ' School of Information Technology, Illinois State University, Campus Box 5150 – Normal, IL 61790-5150, USA ' Mathematics and Computer Science Department, Amar Telidji University, 03000, Laghouat, Algeria

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'.

Keywords: advertisement detection; media monitoring; audio outliers removal; deep learning; autoencoder.

DOI: 10.1504/IJCVR.2022.123848

International Journal of Computational Vision and Robotics, 2022 Vol.12 No.4, pp.397 - 410

Received: 10 Dec 2020
Accepted: 07 Jun 2021
Published online: 04 Jul 2022 *

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