A Deep Learning CNN Model for TV Broadcast Audio Classification
B. Kamatchy, P. Dhanalakshmi · Zenodo (CERN European Organization for Nuclear Research) · 2020
In the media, there are many electronic devices used in our day to day life. Television plays a predominant role. A method using deep learning Convolution Neural Network is introduced here to classify TV programs into one of the five categories namely Advertisement, Cartoon, News, Songs and Sports, based on the analysis of audio content. The objective of this work is to develop a CNN architecture to classify the audio segments significantly. The required dataset is created from different channels of Television using TV tuner card and by downloading from you tube channels. The proposed CNN model gives the accuracy of 95 % for TV broadcast audio classification.